Qwen3-235B-A22B-Thinking-2507 vs Sakana Namazu: Specs & Benchmark Comparison

Qwen3-235B-A22B-Thinking-2507 is developed by Alibaba, while Sakana Namazu comes from Sakana AI. Qwen3-235B-A22B-Thinking-2507 was released in July 2025, and Sakana Namazu followed 13 months later in August 2026. Qwen3-235B-A22B-Thinking-2507 has a published size of about 235 billion parameters; Sakana AI has not disclosed the parameter count of Sakana Namazu.

The two models share 2 published benchmarks. Sakana Namazu leads on 2 of them. The widest gaps are on LiveCodeBench v6, where Sakana Namazu scores 90.3% against 74.1%; MMLU-Pro, where Sakana Namazu scores 90.3% against 84.4%. Averaged across everything we track, Qwen3-235B-A22B-Thinking-2507 sits at 69.2% and Sakana Namazu at 92.4%.

Qwen3-235B-A22B-Thinking-2507 is the cheaper API at $0.30 per million input tokens and $3 per million output tokens, roughly 3 times cheaper than Sakana Namazu at $0.95 and $4. Both accept a context window of about 256K tokens. Qwen3-235B-A22B-Thinking-2507 accepts text as input, while Sakana Namazu accepts text and images. On tooling, only Qwen3-235B-A22B-Thinking-2507 supports function calling and only Qwen3-235B-A22B-Thinking-2507 offers structured output.

CharacteristicQwen3-235B-A22B-Thinking-2507Sakana Namazu
CompanyAlibabaSakana AI
Release DateJuly 24, 2025August 3, 2026
Parameters235B
MultimodalNoYes
Context (input)256K256K
Context (output)33K256K
Input Price / 1M$0.30$0.95
Output Price / 1M$3.00$4.00
Average Score69.2%92.4%
Benchmarks
LiveCodeBench v674.1%90.3%
MMLU-Pro84.4%90.3%

Visual Benchmark Comparison

Qwen3-235B-A22B-Thinking-2507
Sakana Namazu
LiveCodeBench v60.7 vs 0.9
0.7
0.9
MMLU-Pro0.8 vs 0.9
0.8
0.9

Verdict

Both models show equal results — the choice depends on your specific use case.

Overall Performance

Both models show comparable average scores: Qwen3-235B-A22B-Thinking-2507 — 0.7, Sakana Namazu — 0.9.

API Cost

Qwen3-235B-A22B-Thinking-2507 is 1.5x cheaper: input $0.30/1M vs $0.95/1M tokens.

Context Window

Same context size: 256K tokens.

Recency

Sakana Namazu is newer: released 8/3/2026 vs 7/24/2025.

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Frequently Asked Questions

Which is better for coding — Qwen3-235B-A22B-Thinking-2507 or Sakana Namazu?
Direct comparison on the SWE-Bench benchmark is not available. We recommend reviewing other metrics on the comparison page.
Which model is cheaper — Qwen3-235B-A22B-Thinking-2507 or Sakana Namazu?
Qwen3-235B-A22B-Thinking-2507 is cheaper for input: $0.30 per 1M tokens vs $0.95.
Which has a larger context window — Qwen3-235B-A22B-Thinking-2507 or Sakana Namazu?
Qwen3-235B-A22B-Thinking-2507 supports a larger context: 256,000 tokens vs 256,000.

The Qwen3-235B-A22B-Thinking-2507 and Sakana Namazu comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the Qwen3-235B-A22B-Thinking-2507 or Sakana Namazu page. See also the complete list of AI model comparisons.