GLM-5.2 と Claude Fable 5 を実測比較：出力予算、reasoning_tokens、そして 0.8 価格係数

GLM-5.2 vs Claude Fable 5：差が出たのは推論力だけではなく出力予算だった#

この比較は「どちらが絶対に強いか」を決める記事ではありません。実際の API 呼び出しで、GLM-5.2 は出力予算を増やすと数学・物理の推論を正しく返しました。一方で Claude Fable 5 は低い予算でも短く安定し、長い HTML アニメーションではより確実に完走しました。

このテストを見る理由#

The test used the Crazyrouter OpenAI-compatible API rather than a chat UI. That matters because the result was not judged only by prose quality. Each response was checked with operational metadata:

Base URL: https://cn.crazyrouter.com/v1
Endpoint: POST /v1/chat/completions
Models: glm-5.2, claude-fable-5
temperature: 0.2
Test date: 2026-07-06

The important fields were max_tokens, completion_tokens, reasoning_tokens, finish_reason, visible content length, whether the generated HTML was closed, and whether the animation actually moved in a browser.

テストした課題#

The benchmark deliberately mixed three task types:

The first two tasks measured reasoning. The third task measured whether a model can produce a complete artifact, not merely a convincing partial code block.

観測結果#

The most important observation is that GLM-5.2 was not failing the reasoning itself. In the math and physics tasks, it produced correct answers after a larger output budget. The problem was visibility and completion: a request could return HTTP 200 while the user-facing content was empty or incomplete.

For the long Canvas animation, the difference was sharper. GLM-5.2 produced a visible HTML fragment at max_tokens=8000, but it stopped inside JavaScript and did not close the file. Claude Fable 5 completed the HTML at max_tokens=8000; browser validation showed no console errors, an 800x500 canvas, controls, a speed slider, and changedPixels=55090 after 700 ms.

費用対効果の見方#

執筆時点で Crazyrouter の pricing API は glm-5.2 に discount: 0.8 を返しています。つまり、reasoning_tokens と max_tokens をきちんと監視できる用途では、GLM-5.2 はかなり費用対効果の高い選択肢になります。

This is the practical tradeoff:

Do not treat the 0.8 multiplier as a permanent universal price. It is a pricing-data snapshot from Crazyrouter at publication time and should be checked again before a large deployment.

実装時の注意#

Minimal request:

curl https://cn.crazyrouter.com/v1/chat/completions \
  -H "Authorization: Bearer $CRAZYROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.2",
    "messages": [
      {
        "role": "user",
        "content": "Solve the HH expected-flips problem with state equations."
      }
    ],
    "temperature": 0.2,
    "max_tokens": 3200
  }'

To compare Claude Fable 5, keep the same payload and change only the model:

{
  "model": "claude-fable-5"
}

For production-style evaluations, log this shape for every request:

{
  "model": "glm-5.2",
  "max_tokens": 3200,
  "finish_reason": "length",
  "completion_tokens": 3200,
  "reasoning_tokens": 3178,
  "visible_content_chars": 0,
  "html_closed": false,
  "browser_validation": "not_run_incomplete_html"
}

API endpoints should stay clean. Do not add UTM parameters to https://cn.crazyrouter.com/v1. Use tracking only on human-facing article or registration links.

同じ OpenAI 互換リクエストを Crazyrouter で流し、自分のプロンプトで両モデルを比較できます。

https://crazyrouter.com/register?utm_source=crazyrouter_blog&utm_medium=article&utm_campaign=glm52_fable5_budget_cost_20260706&utm_content=crazyrouter_blog_glm-52-vs-claude-fable-5-output-budget-cost-ja_20260706__bottom&utm_term=glm-5.2+claude+fable+5+benchmark

FAQ#

Did GLM-5.2 fail the reasoning tasks?#

No. In this run, GLM-5.2 solved the math task after max_tokens=3200 and the physics task after max_tokens=8000. The issue was that lower budgets were consumed mostly by reasoning tokens before visible content appeared.

Why not score HTTP 200 as success?#

Because HTTP 200 only means the API call returned. A benchmark answer can still be unusable if finish_reason=length, visible content is empty, or generated code is incomplete.

Why was the animation task included?#

Long code generation exposes a different failure mode. A model can write a convincing first half of a file and still fail if the HTML or JavaScript is cut off.

Is GLM-5.2 still worth testing?#

Yes. The current 0.8 discount multiplier makes it attractive for workloads where you can allocate enough output budget and monitor response metadata.

What should be recorded in future comparisons?#

At minimum: max_tokens, completion_tokens, reasoning_tokens, finish_reason, visible output length, artifact completeness, and runtime validation.

Final verdict#

結論は単純ではありません。GLM-5.2 はコスト面で魅力があり推論も可能ですが、出力予算の管理が必要です。Claude Fable 5 は短い回答と完成した単一 HTML 生成で安定していました。