GPT-5.2-Codex vs Sakana Namazu: which should you choose in 2026?
GPT-5.2-Codex (by OpenAI) and Sakana Namazu (by Sakana AI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Sakana Namazu is about 2.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $4.81 per 1M tokens).
GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Choose GPT-5.2-Codex if…
- • you work with longer documents, transcripts, or codebases
Choose Sakana Namazu if…
- • lower blended API cost matters for your workload
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | GPT-5.2-Codex | Sakana Namazu |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | 72.8% SWE-bench | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 74.0% LiveBench | — |
| Arena preference scores | ||
Human preference score for code and web development | 1338 ±9 | — |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 12, 2026.
Pricing, capabilities, and model facts
| Feature | GPT-5.2-Codex | Sakana Namazu |
|---|---|---|
| Context & model facts | ||
| Developer | OpenAI | Sakana AI |
| API provider | OpenAI | Sakana |
| Input context | 400,000 tokens | 262,144 tokens |
| Maximum output | 128,000 tokens | 65,536 tokens |
| Released | — | — |
| Added to Writingmate | Jan 14, 2026 | Aug 11, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text, Image | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.75 | $0.95 |
| Output (per 1M tokens) | $14.00 | $4.00 |
| Blended 3:1 input/output | $4.81 | $1.71 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Aug 11, 2026.
Why Pay for Multiple Subscriptions?
Comparing GPT-5.2-Codex from OpenAI with Sakana Namazu from Sakana AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | GPT-5.2-Codex | Sakana Namazu | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
GPT-5.2-Codex vs Sakana Namazu FAQ
Which is better, GPT-5.2-Codex or Sakana Namazu?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Sakana Namazu is about 2.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $4.81 per 1M tokens). GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
Sakana Namazu is about 2.8× cheaper on a blended 3:1 input/output basis ($1.71 vs $4.81 per 1M tokens).
Which model supports more context?
GPT-5.2-Codex has the larger context window (400,000 tokens vs 262,144 tokens).
Can I switch between GPT-5.2-Codex and Sakana Namazu?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
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