Flagship LLM Upgrades: Evaluation, Cost Recalculation, and Migration Strategy

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A practical framework for teams already running a previous-generation flagship model in production: how to benchmark long-horizon agentic tasks instead of trusting public leaderboards, how to model per-task and per-successful-task cost when only cache-read pricing dropped, how to route traffic across model tiers instead of switching wholesale, and how to instrument and roll back the change safely. Includes evaluation-harness and cost-model code, cache-warming practices, observability fields, and a risk checklist.

Why the Vendor’s Numbers Are Only a Mirror

In September 2026, a leading model vendor shipped a new flagship model (referred to below as the “new version”), while the previous flagship (the “old version”) remains in service. Priced in the same band as its predecessor, the new version shows two clear changes: a substantial jump on long-horizon tasks such as terminal coding and research assistance, and a steep cut to the billing rate for cache reads. The vendor also introduced a trusted-access version with a narrower access scope and more generous limits.

Vendor-published data is only one side of the picture. For teams already running models in production, the real questions are three:

Where is it stronger? Do the capability gains land on your critical path?
Where is it cheaper? Which billing line item did the price cut touch, and does that match your traffic mix?
Should it become the default? Do the switching gains cover the latency, stability, and regression risk?

Deciding from public benchmarks alone leads to wrong conclusions surprisingly easily. This article lays out an executable evaluation framework covering benchmark methodology, cost modeling, migration strategy, and risk governance.

1. Three Common Evaluation Mistakes

Mistake 1: substituting public leaderboards for your own business evaluation. Benchmark items may already be in the training data, and the vendor’s test-time calling configuration is not necessarily how you call the model. More importantly, there is a distribution mismatch: a customer-service system built around single-turn Q&A will gain little from “the model takes fewer shortcuts on long tasks.”

Mistake 2: looking only at unit price. A vendor’s published per-call or average savings figure rests on a specific workload — a fixed cache structure, output volume, and reasoning tier. Shift to your own task distribution and the savings can be fully offset, or even reversed. As shown below, once the default reasoning tier moves up, savings from cheaper cache reads can drop to zero instantly.

Mistake 3: accepting on averages. Average pass rate and average cost hide the tail: long-task timeouts, cost outliers, and regressions caused by drift in behavioral style.

2. Capability Evaluation: Long-Horizon Tasks Are the Real Dividing Line

2.1 The Capability Gap: From “Can Answer” to “Can Finish”

The old version already emphasized multi-hour, multi-step tasks; the new version pushes further into agentic coding and tool-driven research workflows. The significance of these capabilities is best understood through the change in task shape.

A single-turn Q&A completes in one call. A long-horizon task is a closed loop with feedback:

read the codebase → propose a plan → modify code → run tests → read errors → fix

Error accumulates at every step of that loop, and the model works under three constraints at once: a limited context budget, a limited step budget, and a continuously changing intermediate state. That is why the capability gap on long-horizon tasks is far larger than on single-turn Q&A.

A single specific problem gets amplified inside the loop into failure of the whole chain, in four main ways:

Premature completion: claiming done before tests pass, or stopping the investigation after a local compromise;
Symptom patching: patching only what the error message says, without fixing the root cause;
Instruction loss: forgetting or drifting away from the original constraints across multiple rounds of edits;
Over-modification: rewriting unrelated code in passing and introducing hidden regressions.

The most common positive feedback developers report about the new version is that it takes fewer shortcuts on long tasks, is more willing to fix root causes, listens better when instructions are added mid-task, and produces cleaner written explanations. All of that is subjective impression; it has to be turned into measurable metrics before it can enter a decision.

2.2 Evaluation Dimensions and Metric Design

The basic unit of evaluation should not be “one call” but “one complete task.” Recommended dimensions:

Dimension	Core question	Suggested metrics
Task completion quality	Was the problem actually solved?	Automated verification pass rate, manual spot-check pass rate
Root-cause fixing	Was the fix in the right place?	Re-test pass rate, number of new regression cases, whether changes are localized
Long-horizon stability	Can it run the full chain?	Average steps, context occupancy, mid-task abandonment rate
Instruction following	Does it respect added and constraint instructions?	Constraint violation rate, rework count
Code review quality	Are the comments worth reading?	Effective-issue recall, false-positive rate, missed critical-defect rate
Efficiency	Is latency acceptable?	Task-level P50/P95 latency, calls per task
Cost	What does the same task cost?	Cost per task, cost per successful task

Code-review tasks deserve separate evaluation, because their feedback signal is closer to “are the comments worth reading”:

Higher recall, higher precision, and fewer irrelevant style nitpicks mean developers are more willing to read the comments;
Higher latency is a real cost, because review sits on the critical path of the development workflow.

These tasks are well suited to routing by change complexity: complex changes go to the new version, small simple edits go to a faster lightweight tier.

2.3 Building the Evaluation Set, with Sample Code

The evaluation set determines how much you can trust the conclusions. Four principles when building it:

Real sources: sample from your own historical tickets, defect reports, code review records, and test-failure logs.
Stratified sampling: stratify by task type (defect fix, feature implementation, refactor, test generation, code review) and by complexity (files changed, modules affected, historical duration), so the share of each stratum matches production.
Objective verdicts: prefer tasks that can be verified automatically — whether a given unit test passes, whether static checks are clean, whether the patch applies cleanly, whether forbidden files were touched.
Contamination control: use only tasks created after the model’s training cutoff, and rotate items regularly.

Example: a minimal evaluation harness that counts only tasks that actually ran to completion and were verified.

import hashlib
import json
import time
from dataclasses import dataclass, field
@dataclass
class Task:
    """One evaluation task. prompt_hash is used to hit the cache stably; verification logic is implemented by the business team."""
    task_id: str
    kind: str            # bugfix / feature / refactor / test / review
    complexity: str      # S / M / L
    prompt: str
    verify: callable     # verify(workdir) -> bool: run tests, static checks, etc.
@dataclass
class Result:
    passed: bool
    steps: int           # tool calls / interaction rounds
    tokens_in: int = 0   # includes cache reads and cache writes
    tokens_out: int = 0
    elapsed_ms: float = 0.0
    error: str = ""
def run_task(agent, task: Task, max_steps: int = 60, timeout_s: int = 3600) -> Result:
    t0 = time.monotonic()
    usage = {"in": 0, "out": 0}
    try:
        # Keep the system prompt and repo snapshot stable (versioned) so the cache prefix can be reused
        prefix = f"<repo@{task.prompt_hash[:8]}>\n{agent.repo_snapshot()}\n</repo>"
        history = [{"role": "system", "content": prefix},
                   {"role": "user", "content": task.prompt}]
        for step in range(1, max_steps + 1):
            if time.monotonic() - t0 > timeout_s:
                return Result(False, step, usage["in"], usage["out"], elapsed_ms=(time.monotonic() - t0) * 1e3,
                              error="timeout")
            resp = agent.step(history)          # model inference + tool calls
            usage["in"] += resp.usage.input_tokens
            usage["out"] += resp.usage.output_tokens
            history.append(resp.as_message())
            if resp.finished:
                break
        else:
            return Result(False, max_steps, usage["in"], usage["out"], elapsed_ms=(time.monotonic() - t0) * 1e3,
                          error="max_steps_exceeded")
        # Verdict depends only on the final state, not on the model claiming it is "done"
        passed = bool(task.verify(agent.workdir))
        return Result(passed, step, usage["in"], usage["out"], elapsed_ms=(time.monotonic() - t0) * 1e3,
                      error="" if passed else "verify_failed")
    except Exception as exc:                    # timeouts, rate limits, and tool errors all count as failures
        return Result(False, step, usage["in"], usage["out"], elapsed_ms=(time.monotonic() - t0) * 1e3,
                      error=repr(exc))
def evaluate(agent, tasks, weights=None):
    """weights: production share of each task type, used for weighted aggregation so the evaluation distribution does not drift from production."""
    results, groups = [], {}
    for t in tasks:
        r = run_task(agent, t)
        results.append((t, r))
        groups.setdefault((t.kind, t.complexity), []).append(r)
    def agg(rs):
        n = len(rs)
        succ = [r for r in rs if r.passed]
        return {
            "n": n,
            "pass_rate": round(len(succ) / n, 3),
            "p95_latency_s": round(sorted(r.elapsed_ms for r in rs)[int(0.95 * (n - 1))] / 1e3, 1),
            "avg_steps": round(sum(r.steps for r in rs) / n, 1),
            "avg_in_tok": round(sum(r.tokens_in for r in rs) / n),
            "avg_out_tok": round(sum(r.tokens_out for r in rs) / n),
            "errors": {e: sum(r.error == e for r in rs) for e in {r.error for r in rs} if e},
        }
    report = {"overall": agg([r for _, r in results]),
              "by_group": {f"{k}/{c}": agg(v) for (k, c), v in sorted(groups.items())}}
    if weights:
        # Weight pass rate by production share; closer to real user experience than a flat overall mean
        num = den = 0.0
        for (k, c), rs in groups.items():
            w = weights.get(f"{k}/{c}", 0.0)
            num += w * sum(r.passed for r in rs) / len(rs)
            den += w
        report["weighted_pass_rate"] = round(num / den, 3) if den else None
    return report

Three things about this harness are worth emphasizing:

Failures count too: timeouts, budget overruns, and exceptions are all failures, which avoids the survivorship bias of counting only tasks that ran to completion.
Token and step instrumentation end to end: these are the direct inputs to the cost model in the next section and to budget design on the application side.
Weighting by production share: overall metrics are easily inflated by simple tasks; only the weighted figure reflects the real user experience.

2.4 How to Read Public Benchmarks

Public leaderboards still carry signal, but you have to “translate” them into your own scenarios by task type:

Terminal and agentic coding: the largest gain, and it directly determines whether “let an agent work continuously for hours” is reliable. The payoff is most direct if you have needs like automated infrastructure inspection, bulk defect fixing, or migration and upgrade work.
Research and exploratory agents: an equally significant gain. For workflows requiring many tool-calling rounds — literature review, data analysis, hypothesis validation — the benefit shows up as fewer dead loops and more reliable intermediate conclusions.
General knowledge tasks: mostly at parity or slightly ahead. Existing systems do not necessarily need to switch; this is better treated as a reason to adopt for new capabilities.
Trusted-access version: more generous limits make it suitable for long-running exploratory tasks. It is generally offered through vetted access arrangements, so a security assessment must be completed before adoption, with clear data boundaries and scope of use.

3. Cost Recalculation: What Got Cheaper Is the Cache Read, Not the List Price

3.1 Break Down the Billing Structure First

The new version continues the strategy of “list price unchanged, cache-read rate cut sharply.” Judging whether it saves money requires first seeing what token billing actually includes:

Line item	When it occurs	Effect on cost
Regular input	Prompts that miss the cache, tool returns, error logs	Grows linearly with context length
Cache write	First write or update of a reusable prefix	Usually slightly above regular input, but amortized over many calls
Cache read	The portion that hits a reusable prefix	The main target of this price cut
Output (including reasoning)	Replies, patches, tool-call arguments, intermediate reasoning	Highest unit price, and the main variable on long tasks

Note that not every vendor bills intermediate reasoning separately. Some count only final output tokens and do not charge for the chain of thought itself. But the reasoning chain still consumes context window and lengthens latency. Either way, unproductive thinking must be controlled.

3.2 Per-Task Cost Model

Suppose a task involves N calls, with total token volumes X (regular input), W (cache write), R (cache read), and Y (output) at unit prices p. Cost per task is:

C = (p_in·X + p_cw·W + p_cr·R + p_out·Y) / 1,000,000

Looking closer, at a cache hit rate h the effective unit price of the input portion is a weighted average:

p_in,eff = h·p_cr + (1−h)·p_in

Let r be the size of the cache price cut (for example, if the unit price drops to one quarter of the original, r = 0.75). The share saved on the cached portion is:

ΔC_in / C_in = h·r

Two conclusions follow:

The maximum saving on the input portion equals the size of the price cut, and you only approach that ceiling when the hit rate is near 100%.
The lower input’s share of total task cost, the weaker the overall effect of the cache price cut. Output-heavy tasks (long code generation, long report writing) are exactly this case.

That gives three task classes:

Context-repetitive: code review, bulk defect fixing. Every call carries a large amount of repeated repository context, so the cache price cut pays off most here.
Output-intensive: generating a module from scratch, writing technical documentation. Input is a small share, savings are limited, and the focus should be compressing unproductive output.
Exploratory: repeated trial and error with lots of output, intermediate reasoning, and retries. Cost per task is highly sensitive to the reasoning tier.

3.3 Worked Example: Whether You Save Depends on Three Assumptions

The function below computes cost per task and models the vendor’s typical rate structure: cache reads at 1/10 of the input price, cache writes at 1.25x. Unit prices are illustrative and only serve to demonstrate the calculation.

from dataclasses import dataclass
@dataclass(frozen=True)
class PriceCard:
    """List price, in CNY per million tokens; illustrative values, the official current list price prevails."""
    pin: float = 10.0
    p_cw: float = 12.5   # cache write = 1.25 x input price
    p_cr: float = 1.0    # cache read = 0.1 x input price
    p_out: float = 50.0
@dataclass
class Workload:
    calls: int = 20            # model calls within the task
    ctx_per_call: int = 40_000 # context size per call
    cache_hit: float = 0.7     # average cache hit rate excluding the first call
    out_per_call: int = 2_000  # output (including reasoning) tokens per call
def task_cost(p: PriceCard, w: Workload, cache_read_factor: float = 1.0) -> dict:
    """cache_read_factor: multiplier on the baseline cache-read unit price; 1.0 is baseline, 0.25 means cut to 1/4."""
    p_cr = p.p_cr * cache_read_factor
    # The first call has no cache to hit; it is billed entirely as input or cache write
    first = w.ctx_per_call
    rest = w.calls - 1
    fresh = first + int(rest * w.ctx_per_call * (1 - w.cache_hit))  # the miss portion
    written = int(rest * w.ctx_per_call * w.cache_hit)              # one cache write per call
    read = int(rest * w.ctx_per_call * w.cache_hit)                 # the same content is read back
    out = w.calls * w.out_per_call
    m = 1_000_000
    parts = {
        "input": fresh * p.pin / m,
        "cache_write": written * p.p_cw / m,
        "cache_read": read * p_cr / m,
        "output": out * p.out / m,
    }
    return {**{k: round(v, 3) for k, v in parts.items()}, "total": round(sum(parts.values()), 3)}
base = Workload()
scenarios = {
    "baseline (old price structure)": dict(),
    "cache read cut to 1/4 only": dict(cache_read_factor=0.25),
    "cache read at 1/4 + hit rate 0.7 -> 0.85": dict(cache_read_factor=0.25, cache_hit=0.85),
    "cache read at 1/4 + default tier raised (output x2)": dict(cache_read_factor=0.25, out_per_call=4_000),
    "cache read at 1/4 + hit rate and output both worse": dict(cache_read_factor=0.25, cache_hit=0.5, out_per_call=4_000),
}
for name, kw in scenarios.items():
    c = task_cost(PriceCard(), Workload(**{**base.__dict__, **kw}) if kw else base,
                  cache_read_factor=kw.pop("cache_read_factor", 1.0))
    print(name, c)

Results (CNY per task):

Scenario	Regular input	Cache write	Cache read	Output	Total	vs. baseline
Baseline (old price structure)	0.110	0.500	0.500	2.000	3.110	—
Cache read cut to 1/4 only	0.110	0.500	0.125	2.000	2.735	−12.1%
Cache read at 1/4 + hit rate 0.7→0.85	0.080	0.606	0.152	2.000	2.837	−8.8%
Cache read at 1/4 + default tier raised (output ×2)	0.110	0.500	0.125	4.000	4.735	+52.3%
Cache read at 1/4 + hit rate and output both worse	0.140	0.358	0.089	4.000	4.587	+47.5%

Several patterns emerge:

Savings are not the price cut. Cache reads are only 16% of baseline cost, so even taking that line to zero lowers the total by roughly 16%.
A higher hit rate does not automatically save money. A longer cached prefix means more write overhead. A hit-rate increase only produces a net gain when the same context is reused enough times. That is why row three goes the wrong way. Because cache writes cost more than regular input, deep cache use suits tasks that genuinely reuse the same context repeatedly.
Doubling output swallows the entire saving. If moving to the new version raises the default reasoning tier and doubles output tokens, cost per task rises by more than 50%. This matches some independent analyses: at the highest reasoning tier with more output, per-task cost is not necessarily lower.
Cache invalidation is a hidden tax. Caches typically hit on an exactly matching prefix. Changing one word of the system prompt, or reordering the tool list, can invalidate an entire cached span. Prompt templates must be versioned and repository snapshots must be content-addressed — see section 4.

3.4 From Cost per Task to Cost per Successful Task

Tasks have a failure rate, and the two versions tend to fail on different tasks, so the more meaningful metric is cost per successful task:

cost per successful task = cost per task ÷ task success rate

For example, suppose the new version cuts cost per task by 12% while raising the success rate on long tasks from 70% to 85%:

0.88 ÷ 0.70 ≈ 1.26，0.85 × 1.26 ≈ 1.07

In other words, the cost of completing one successful task actually drops by about 7%. For agentic products, completion rate is itself a core business metric. The new version’s value may therefore show up more in “can finish the job” than in “is cheaper.”

Conversely, if the new version repeatedly self-checks in pursuit of thoroughness on low-complexity tasks, producing more output and longer latency without improving the first-pass rate, cost per successful task rises. Whether it pays off has to be settled with numbers:

net benefit = Δ(cost per successful task) × monthly task volume + Δ(manual rework hours) × labor cost − migration and regression-testing investment

Only when net benefit is positive and the payback period is acceptable does switching make economic sense.

3.5 Engineering Practices That Make Caching Actually Work

Caching pays off on “reusable stable prefixes.” Four things are needed:

Stable prefix, variable suffix: put the system prompt, tool definitions, and repository index first and keep them stable; put the user question and real-time data last.
Prefix versioning: bring prompts and tool lists under version control and bump the version number on any change. This keeps the cache prefix consistent and makes behavior drift easier to locate.
Content-addressed repository snapshots: name snapshots by content hash. If content is unchanged, the cache keeps hitting; if content changes, the cache naturally invalidates, avoiding accidental reuse of stale cache.
Manage cache lifetime: caches usually have a TTL, and frequently used context must stay active within that window. Batch jobs should warm the cache first, then run in a concentrated pass.

A reference skeleton:

import hashlib
import json
from dataclasses import dataclass
@dataclass(frozen=True)
class PromptBundle:
    prompt_version: str            # prompt version, monotonically increasing on change
    tool_version: str              # tool list version
    system_block: str              # stable system prompt
    repo_snapshot: dict            # {file path: content}
    tools: list                    # tool definitions, fixed order
    @property
    def cache_key(self) -> str:
        payload = {"pv": self.prompt_version, "tv": self.tool_version,
                   "repo": self.repo_snapshot, "tools": self.tools}
        blob = json.dumps(payload, sort_keys=True, ensure_ascii=False, separators=(",", ":"))
        return hashlib.sha256(blob.encode()).hexdigest()[:16]
    def prefix(self) -> str:
        """Stable prefix: version numbers + snapshot identifier + system prompt + tool list."""
        return f"[prompt@{self.prompt_version}|tools@{self.tool_version}|repo@{self.cache_key[:8]}]\n" \
               f"{self.system_block}\n{json.dumps(self.tools, sort_keys=True, ensure_ascii=False)}"
def warmup(client, bundle: PromptBundle) -> bool:
    """Warm the cache before a batch job starts; returns whether it hit, for monitoring hit rate."""
    resp = client.create(messages=[{"role": "user", "content": bundle.prefix() + "\nping"}], max_output_tokens=1)
    return resp.usage.cache_read_tokens > 0

4. Migration Decision: Not “Switch Everything” but “Tiered Routing”

4.1 A Three-Layer Test

Layer 1: does the capability gain land on the critical path? Look at the production task distribution and find the slowest steps, the ones with the most rework, and the ones with the highest failure rate. If those are long-horizon tasks, migration pays off; if the workload is mostly single-turn short tasks, prioritize latency and cost optimization instead.

Layer 2: is the cost structure favorable? Feed your real traffic into the model from section 3: average context length, reusable share, average output size, expected reasoning tier. If input’s share is low or the cache hit rate is low, the savings are diluted.

Layer 3: are engineering and compliance ready? Check the following:

Can response latency meet the timeout budgets of upstream and downstream systems?
Can the model version be pinned, and can you roll back quickly?
Is token usage observable end to end?
Do data boundaries and access permissions meet requirements? Trusted-access versions in particular need to be brought under unified approval and audit.

Only when all three layers pass is a model suitable as the default tier.

4.2 A Tiered Routing Gateway

Implement per-task routing in a unified gateway so that model selection is a configurable policy rather than hardcoded logic scattered across business code.

from dataclasses import dataclass
@dataclass(frozen=True)
class Tier:
    model: str
    reasoning_effort: str  # reasoning tier: low / medium / high
    timeout_s: int
class Router:
    """Pick a tier by task type, change size, and context volume; the strategy can be hot-reloaded from a config center."""
    def __init__(self, tiers: dict, limits: dict, strategy: dict):
        self.tiers, self.limits, self.strategy = tiers, limits, strategy
    def pick(self, task: dict) -> Tier:
        kind, n_files, ctx_tokens = task["kind"], task.get("changed_files", 1), task["ctx_tokens"]
        # 1) Lightweight tasks always go to the small tier; no need for a sledgehammer
        if kind in ("commit_msg", "rename", "lint_fix", "docstring"):
            return self.tiers["small"]
        # 2) Only exploratory, long-horizon tasks use the flagship tier, with a per-task budget cap
        if kind in ("bugfix", "feature", "refactor", "research") and n_files >= self.limits["flagship_min_files"] \
                or ctx_tokens >= self.limits["flagship_min_ctx"]:
            return self.tiers["flagship"]
        # 3) Everything else goes to the mid tier, with reasoning effort adapting to complexity
        if kind == "review" and n_files <= 3:
            return self.tiers["mid"]
        effort = "high" if (n_files >= 5 or task.get("risk") == "high") else "medium"
        return Tier(self.tiers["mid"].model, effort, self.tiers["mid"].timeout_s)

Three supporting policies are needed:

Fallback: on rate limiting or upstream errors, temporarily downgrade one tier and retry, tagging the result with its source so evaluation can distinguish it later.
Budget: set a step cap, an output-token cap, and a timeout per task so individual tasks cannot burn cost out of control.
Guardrails: uniformly intercept prompt injection, unauthorized tool access, and reads of sensitive paths, with one policy set shared across all tiers.

5. Observability and Governance: Make the Switch Measurable and Reversible

5.1 End-to-End Observability

Routing and call logs should record the following fields in structured form and feed them into monitoring and alerting:

{
  "trace_id": "7f3c…",
  "task_kind": "bugfix",
  "tier": "flagship",
  "reasoning_effort": "medium",
  "prompt_version": "v12",
  "cache_key": "9ab1…",
  "input_tokens": 18240,
  "cache_write_tokens": 0,
  "cache_read_tokens": 32000,
  "output_tokens": 3120,
  "latency_ms": 184_500,
  "outcome": "verified_pass",
  "retry_of": null
}

From these fields you can compute at least four metrics:

Cache hit rate: cache reads ÷ (cache reads + regular input)
Output inflation: new-version output tokens ÷ old-version output tokens, on the same task basis
Tier distribution and cost per successful task per tier
Task-level latency P50 and P95

5.2 A Staged Rollout Process

A five-step rollout:

Shadow mode: the new version receives real traffic, but its results are not returned to users; they are only logged and compared offline.
Small-traffic canary: ramp up by task type, for example starting with internal repositories before covering core repositories.
Offline regression: every time a prompt or tool list changes, re-run the evaluation set from section 2 as a release gate.
Human blind review: for tasks that are hard to judge automatically, such as code review and document generation, have reviewers score output without knowing which model produced it.
Rollback plan: keep one-click reversion to the old version in the gateway, so version switching does not depend on redeploying business code.

In enterprise deployments, this architecture typically sits behind a unified model-invocation entry point, paired with object storage for repository snapshots and evaluation samples, a logging service for call-level detail, and monitoring and alerting for key metrics. For example, on Baidu AI Cloud you can assemble a model gateway, an evaluation pipeline, and a cost dashboard from these general-purpose capabilities.

5.3 Behavior Drift and Version Pinning

Model behavior drifts as versions change — output format shifts, tool-calling style shifts, and the interpretation of implicit constraints in a prompt shifts. The countermeasures:

Pin the model version and put upgrades through a review process;
Keep system prompts and tool definitions under version control, and parse output in structured formats where possible;
Automatically retry and downgrade when parsing fails or the format is non-compliant.

5.4 Risk Checklist

Risk	Typical symptom	Mitigation
Output inflation	Longer chains of thought, wider patch scope, higher per-task cost	Set output budgets, constrain change scope, account on a per-successful-task basis
Higher latency	Task-level P95 latency rises and trips upstream timeouts	Set task-level timeouts, make long tasks asynchronous, route by complexity
Cache invalidation	A small prompt or tool-list tweak drops the hit rate sharply	Prefix versioning, content-addressed snapshots, hit-rate alerting
Over-automation	The agent autonomously executes high-risk commands	Sandbox isolation, human confirmation for dangerous operations, least privilege
Data boundaries	A trusted-access version handles sensitive data	Redaction, access approval, audit trails
Stale evaluation set	Items leak or age out and scores look inflated	Rotate items regularly, use only tasks created after the cutoff date

Conclusion

Back to the three opening questions:

Where is it stronger? Mainly on long-horizon, multi-step, tool-driven tasks such as agentic coding and research assistance. Whether you benefit depends on how large a share those tasks are in your workload.
Where is it cheaper? Mainly in cache-read billing, with gains positively correlated to cache hit rate and to input’s share of total cost. Output and reasoning tokens are the real cost driver on long tasks, so “savings” must be computed against the actual distribution of each task.
Should it be the default? Follow the path of “validate with an evaluation set → compare in shadow mode → route in tiers → ramp up gradually.” Switch first where the capability gain is largest, and keep a fast tier for lightweight tasks.

For teams still on the previous-generation flagship, four things can start now:

Map the last 30 days of task distribution and cost composition, and work out how much is input, output, and cache.
Build a stratified evaluation set from real historical tasks, with automated verification plus timeout and exception handling.
Feed real parameters into the cost model and compute both cost per task and cost per successful task.
Stand up a unified gateway with cache observability, laying the groundwork for staged rollout and fast rollback.

Model iteration will keep accelerating. Sustainable competitiveness comes not from being first to adopt the newest model, but from having an engineering system that can evaluate quickly, account precisely, and switch safely.