1. Overall Positioning: From “It Runs” to “It Works Like a Person”

The capability leap of this new generation of open models can be understood through a simple analogy: the previous generation was like an “uncrewed lunar flyby,” validating the feasibility of the basic trajectory; this generation is closer to a “crewed lunar flyby”—not only must it fly, but it must demonstrate stable judgment and execution in real tasks. This shows up in two ways. First, when handling real problems and executing long-horizon tasks, its behavior pattern is closer to how human engineers work: decompose first, then plan, and verify while executing. Second, reasoning capability has improved markedly, returning to the top tier within the domestic model lineup.

The capability improvement also brought a slight adjustment to pricing strategy, with unit prices rising modestly. However, because the chain-of-thought structure of this generation has not changed much, overall inference cost remains in a controllable range. This is especially important for enterprises making large-scale calls: if capability gains do not come with drastic changes in cost structure, it means better task completion rates can be achieved within an existing budget framework.

2. Long-Chain Reasoning: Fuller Solution-Space Exploration, but Thinking Efficiency Becomes the New Bottleneck

2.1 The Previous Generation’s Core Weakness: Insufficient Solution-Space Exploration

On various long-chain logic puzzles and multi-step reasoning tasks, the previous generation did not score poorly, but it had a typical problem: when facing problems with multiple solutions, its exploration of the solution space was insufficient. Put simply, the model often stopped searching once it found the first “seemingly valid” solution, rather than systematically traversing other possible solution paths. In logic puzzles this appeared as missed solutions; in engineering tasks it could appear as “a solution that runs but is not optimal” or even “missed boundary conditions.”

2.2 This Generation’s Improvement: Trading Budget for Completeness

The new generation basically solves the above problem, being able to explore various possibilities fairly thoroughly and find as many solutions as possible. The cost is that the model tends to run at the upper limit of its thinking budget. Against the backdrop of overall token consumption staying roughly flat versus the previous generation, per-question consumption on complex logic puzzles may instead reach several times that of the previous generation.

This reveals an engineering fact worth attention: unchanged average cost does not mean unchanged cost distribution. Budget saved on simple questions is consumed by deep exploration on complex questions. For business scenarios with a skewed task-difficulty distribution (for example, many simple queries plus a small number of high-difficulty analyses), the actual billing structure may deviate noticeably from expectations.

2.3 The Gap with Leading Solutions: Efficiency, Not Capability

On the same questions and with the same full score, leading industry solutions consume significantly less—in some scenarios only one-half or even one-sixth as much. This means the main gap has shifted from “whether it can get it right” to “how much it costs to get it right.” How to increase the effective thinking density per token—that is, improve thinking efficiency—is a key problem the next generation needs to address.

The practical takeaway for developers is: in model-selection evaluation, do not look only at accuracy as a single metric; build a two-dimensional “accuracy vs. token consumption” evaluation matrix. Below is a simplified evaluation script:

def evaluate_model(cases, model_client, token_budget):
    results = []
    for case in cases:
        resp = model_client.invoke(
            prompt=case.prompt,
            max_output_tokens=token_budget,
            mode="slow_thinking"   # reasoning mode
        )
        results.append({
            "case_id": case.id,
            "correct": judge(resp.answer, case.gold),
            "tokens": resp.usage.output_tokens,
            "truncated": resp.usage.output_tokens >= token_budget
        })
    acc = sum(r["correct"] for r in results) / len(results)
    avg_tokens = sum(r["tokens"] for r in results) / len(results)
    trunc_rate = sum(r["truncated"] for r in results) / len(results)
    return {"accuracy": acc, "avg_tokens": avg_tokens, "truncation_rate": trunc_rate}

Here truncation_rate is a metric many evaluations overlook but is extremely critical—it directly reflects how well the model’s output length matches the budget ceiling.

3. The “Implicit Reasoning” of Non-Reasoning Mode and Its Evaluation Dilemma

The non-reasoning mode of this generation shows a noteworthy behavioral change: it behaves more like an “adaptive reasoning model with a smaller thinking budget.” Simple questions can still be solved within a few thousand tokens, but on complex tasks output consumption can quickly balloon to the 20,000 to 30,000 token range.

This brings two direct consequences:

First, the output ceiling mismatches task requirements. Because the output token ceiling set for non-reasoning mode is relatively low, nearly half of the test samples exceeded that limit. Under the evaluation rules, the excess portion is not scored, so non-reasoning mode temporarily cannot complete the full test. This is not a model capability issue but an adaptation issue between the evaluation framework and the model’s behavioral characteristics.

Second, reasoning mode is also affected by overly long chains of thought. A small number of questions exceeded the set ceiling in multiple test runs and could only be scored zero under the rules, which is one reason the median score in reasoning mode is lower. In other words, part of the score loss comes not from wrong answers but from “saying too much.”

The implications for engineering practice are clear:

• When integrating such models, the output length ceiling must be treated as a first-class configuration item, not left at default values;
• For long-output scenarios, design mechanisms such as segmented continuation and summary return to avoid single-call truncation;
• Evaluation systems need to introduce “truncation attribution” analysis to distinguish “insufficient capability” from “insufficient budget” failures.

4. Agent Coding: From One-Shot Generation to Real Project Deployment

4.1 The Previous Generation’s Positioning

The previous generation improved substantially in one-shot coding capability, but performed poorly in the combined scenario of “real Agent + real project.” The core weakness was the lack of effective debugging means—that is, after code execution failed, the model lacked a systematic closed loop of localization, hypothesis, verification, and repair.

4.2 This Generation’s Progress

In early public preview versions, agent programming capability has already improved significantly. For conventional front-end and back-end programming tasks alone, its capability has entered the “usable” range, outperforming mainstream coding models of the same tier on most standard tasks. “Usable” here should be understood as: capable of end-to-end implementation of moderately complex features, with some self-correction ability when errors occur.

4.3 An Evaluation Framework for Agent Coding Capability

Agent coding capability in real projects cannot be measured only by “whether it can generate correct code.” It is recommended to build evaluations across the following dimensions:

1. Task decomposition: Can it break requirements into independently verifiable subtasks?
2. Context management: Can it maintain accurate memory of project structure, dependencies, and conventions across multi-turn interactions?
3. Debugging closed loop: After failure, can it propose verifiable hypotheses rather than retrying blindly?
4. Change impact control: When modifying one piece of code, can it identify and synchronously handle affected callers?
5. Deliverability: Does the output include necessary error handling, boundary validation, and readability guarantees?

In real engineering, you can set a bounded debugging loop for the Agent to avoid wasting budget on ineffective retries:

MAX_DEBUG_ROUNDS = 5
for round_idx in range(MAX_DEBUG_ROUNDS):
    patch = agent.propose_fix(error_log=last_error, context=repo_context)
    apply_patch(patch)
    ok, last_error = run_tests()
    if ok:
        break
    if is_same_error(last_error, previous_error):
        agent.escalate("Repeated failure; re-analyze the root cause instead of fine-tuning")

“Repeated failure detection” is key to controlling cost: if error messages show no substantive change across consecutive rounds, a strategy switch should be triggered rather than continued budget consumption.

5. Deployment Recommendations and Follow-Up Focus Areas

Overall, the capability improvement of this generation is real, but the engineering side needs to adjust supporting strategies accordingly:

• Mode-specific selection: Use non-reasoning mode for latency-sensitive, simple tasks; enable reasoning mode for multi-step logic and complex analysis scenarios, and reserve a higher output budget for the latter.
• Layered budget management: Set token ceilings by task difficulty tier, combined with truncation detection and automatic retry/degradation strategies.
• Build your own evaluations: General leaderboards can only serve as reference; enterprises should build three-dimensional evaluation sets covering accuracy, consumption, and truncation rate based on their own business distribution.
• Watch thinking efficiency: The core competitive point in the next stage is “consumption optimization at equal accuracy”; model selection should continuously track trends in this dimension.

Overall, this generation is closer to the requirements of real engineering collaboration in its behavior pattern on long-horizon tasks, with clear progress in both the completeness of long-chain reasoning and the usability of agent coding; how to keep capability while driving down thinking overhead is the direction that needs continued observation.