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Model Comparison

GPT-5.3 Codex vs Sakana NamazuWhich Is Better in 2026?

OpenAI: GPT-5.3-Codex logoOpenAI•
Sakana AI

GPT-5.3 Codex vs Sakana Namazu: which should you choose in 2026?

GPT-5.3 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-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).

Choose GPT-5.3 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.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

GPT-5.3 Codex and Sakana Namazu benchmark results
BenchmarkGPT-5.3 CodexSakana Namazu
AutomationBench-AA

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

——
BrowseComp

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

77.3%
OpenAI report
—
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

74.0%
OpenAI report
—
SWE-Bench Pro

A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols.

Resolved

56.8%
OpenAI report
—
SWE-Bench Verified

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

——
SWE-Lancer (IC-Diamond subset)

The individual-contributor Diamond subset of SWE-Lancer, which evaluates economically valuable real-world software-engineering tasks.

Tasks completed

81.4%
OpenAI report
—
Terminal-Bench 2.0

Version 2.0 of the benchmark for completing realistic tasks in terminal environments. Results must not be merged with Terminal-Bench 2.1.

Pass rate

77.3%
OpenAI report
—
Terminal-Bench 2.1

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

79.1%
Terminal-Bench
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AutoBench

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

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

92.6%
OpenAI report
—
Humanity's Last Exam

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

——
Cybersecurity CTFs

Capture-the-flag challenges used to evaluate an agent's practical cybersecurity task performance.

Challenges completed

77.6%
OpenAI report
—
Arena preference scores
Arena (Code/WebDev)

Human preference score for code and web development

Ambiguous / conflicting rows—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 12, 2026.

Pricing, capabilities, and model facts

GPT-5.3 Codex and Sakana Namazu model facts
FeatureGPT-5.3 CodexSakana Namazu
Context & model facts
DeveloperOpenAISakana AI
API providerOpenAISakana
Input context400,000 tokens262,144 tokens
Maximum output128,000 tokens65,536 tokens
ReleasedFeb 5, 2026—
Added to WritingmateFeb 24, 2026Aug 11, 2026
LicenseProprietaryNot available
Knowledge cutoffNot disclosed—
Capabilities
InputsText, Image, FileText, Image, File
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
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 latencyNot measuredNot measured
Output throughputNot measuredNot 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

Catalog data last updated Aug 11, 2026.

Why Pay for Multiple Subscriptions?

Comparing GPT-5.3 Codex from OpenAI with Sakana Namazu from Sakana AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for GPT-5.3 Codex and Sakana Namazu
PlanPriceGPT-5.3 CodexSakana NamazuAI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

GPT-5.3 Codex vs Sakana Namazu FAQ

Which is better, GPT-5.3 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-5.3 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-5.3 Codex has the larger context window (400,000 tokens vs 262,144 tokens).

Can I switch between GPT-5.3 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.