LLM Inference Efficiency and Agent Coding: Evolution Observations

1. Background: From “Can Answer” to “Handles Long-Horizon Tasks Like a Human”

A new generation of an open-source LLM was recently released. It arrived only a short time after the previous generation, but its capability positioning has changed noticeably. One analogy: the previous generation was like an “uncrewed lunar flyby,” validating basic orbital and return capabilities; this generation is closer to a “crewed lunar flyby,” beginning to show stronger stability and a more human-like feel when handling real problems and long-horizon tasks.

This shift is mainly reflected in three areas:

1. Significantly improved reasoning: Across multiple logic benchmarks, this version has returned to the top tier, reclaiming positions previously held by other models.
2. Modest price increase: The unit price rose from 21 to 27, a mild increase. The confidence comes from capability gains, while the chain-of-thought structure has not changed much, so total cost remains controllable.
3. Changed behavior in non-reasoning mode: This generation’s non-reasoning mode is no longer a “fast direct output” model; it is more like an adaptive reasoning model with a smaller thinking budget. Simple questions are resolved within a few thousand Tokens, while complex tasks may output 20K or even 30K.

However, this “adaptiveness” also introduces new engineering problems: non-reasoning mode has a 15K Token cap, and nearly half of complex tasks exceed that limit and cannot be scored under the rules, so non-reasoning mode has not yet been fully tested. Reasoning mode is also affected by overly long chains of thought; a small number of problems exceed the 80K cap across multiple test runs and can only be scored as 0, which is also why the median score in reasoning mode is lower.

Below, we compare the previous and new generations on two dimensions: long-chain reasoning and coding capability.

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

2.1 Strengths: Clear Improvement on Multi-Step Logic and Multi-Solution Problems

On various long-chain logic and multi-step reasoning tasks, the new generation performs significantly better than the previous one. The previous generation did not score poorly on the same problems, but had a typical issue: insufficient exploration of the solution space when multiple solutions exist. In other words, the model could find some solutions but easily missed other feasible paths.

The new generation largely resolves this, exploring possibilities more fully and finding as many solutions as possible. This matters especially for tasks requiring enumeration, planning, and constraint satisfaction.

But the cost is direct:

• The new generation runs right up against the thinking budget cap;
• With overall Token consumption on par with the previous generation, complex logic problems may consume more than 3x as many Tokens as before;
• Compared with leading industry models, on the same problems and with the same full score, leading models consume only 1/2 or even 1/6 of the new generation’s Tokens.

In other words, capability has improved, but reasoning efficiency has not improved in tandem. How to improve reasoning efficiency is the core problem the next generation must address.

2.2 The Impact of Thinking Budgets and Scoring Rules

To control costs, evaluations typically set caps on output Tokens. For example:

• Non-reasoning mode: 15K cap;
• Reasoning mode: 80K cap.

When model output exceeds the cap, it cannot be scored or is scored as 0 under the rules. This leads to two phenomena:

1. Incomplete testing of non-reasoning mode: Nearly half of complex tasks exceed 15K and cannot be scored.
2. Lower median score in reasoning mode: A small number of problems exceed 80K across multiple runs and can only be scored as 0, dragging down the median.

This reminds us that evaluation scores reflect not only capability but also whether the model can complete tasks within budget. For production environments, the latter is often more important.

3. Coding Capability: From Oneshot to Real Agent Projects

3.1 The Previous Generation’s Problem: Better Oneshot, but Struggles on Real Projects

The previous generation substantially improved oneshot coding—generating code in a single pass. But in real agent plus real project scenarios, performance was unsatisfactory, lacking effective debugging means.

Specifically:

• Generated code could run, but errors were hard to locate after they occurred;
• Context was easily lost across multi-turn interactions;
• Understanding of project structure and dependencies was insufficient.

3.2 Progress in the New Generation: Agent Coding Enters the “Usable” Range

In the earlier public preview version, agent coding capability improved significantly. Looking only at conventional frontend and backend coding, its capability already surpasses certain mainstream mid-sized models and has reached the “usable” range.

This means:

• In real projects, the model can complete module-level development more stably;
• Debugging means have been strengthened, allowing corrections based on error messages;
• Context retention is better during multi-turn collaboration.

Of course, “usable” does not mean “production-grade with no concerns.” In complex projects, legacy code, cross-service calls, and similar scenarios, human intervention and engineering safeguards are still required.

4. Engineering Practice Recommendations: How to Make Good Use of the New Generation of Reasoning Models

4.1 Distinguish Fast-Thinking and Slow-Thinking Scenarios

• Simple Q&A, classification, extraction: Use non-reasoning mode, control output length, and avoid triggering high consumption.
• Multi-step reasoning, planning, code generation: Use reasoning mode, but set a reasonable Token cap to avoid unbounded thinking.

4.2 Set Reasonable Budgets and Fallback Strategies

# Pseudocode: dynamically choose mode based on task complexity
def choose_mode(task):
    if task.complexity == "simple":
        return "non_reasoning", max_tokens=4096
    elif task.complexity == "medium":
        return "non_reasoning", max_tokens=8192
    else:
        return "reasoning", max_tokens=32768

When output approaches the cap, you can trigger a fallback: truncate, summarize, or switch to a smaller model.

4.3 Monitor Token Consumption and Success Rates

In production environments, it is recommended to record:

• Average Token consumption per task type;
• Proportion of over-limit cases;
• Final success rate.

Only then can you judge whether the model is truly “usable” rather than merely looking good on a benchmark leaderboard.

5. Conclusion

The new generation of the open-source LLM has made clear progress in both long-chain reasoning and agent coding: solution-space exploration is fuller, and coding capability in real projects has entered the usable range. But reasoning efficiency remains a weakness, and Token consumption on complex logic problems may be several times that of leading models. For developers and enterprise users, the key is to choose the appropriate mode and budget based on task complexity, and to continuously monitor consumption and success rates in production. Only in this way can you keep costs controllable and systems stable while capability improves.