DeepSeek-V3.2 (Thinking) vs Sakana Namazu: Specs & Benchmark Comparison

DeepSeek-V3.2 (Thinking) is developed by DeepSeek, while Sakana Namazu comes from Sakana AI. DeepSeek-V3.2 (Thinking) was released in November 2025, and Sakana Namazu followed 9 months later in August 2026. DeepSeek-V3.2 (Thinking) has a published size of about 685 billion parameters; Sakana AI has not disclosed the parameter count of Sakana Namazu.

We have 14 benchmark results for DeepSeek-V3.2 (Thinking) and 3 benchmark results for Sakana Namazu, but they overlap on a single test: MMLU-Pro, where Sakana Namazu scores 90.3% against 85.0%.

DeepSeek-V3.2 (Thinking) is the cheaper API at $0.28 per million input tokens and $0.42 per million output tokens, roughly 3 times cheaper than Sakana Namazu at $0.95 and $4. Sakana Namazu takes the larger context window at 256K tokens, compared with 131K for DeepSeek-V3.2 (Thinking). DeepSeek-V3.2 (Thinking) accepts text as input, while Sakana Namazu accepts text and images. On tooling, only DeepSeek-V3.2 (Thinking) supports function calling and only DeepSeek-V3.2 (Thinking) offers structured output.

CharacteristicDeepSeek-V3.2 (Thinking)Sakana Namazu
CompanyDeepSeekSakana AI
Release DateNovember 30, 2025August 3, 2026
Parameters685B
MultimodalNoYes
Context (input)131K256K
Context (output)66K256K
Input Price / 1M$0.28$0.95
Output Price / 1M$0.42$4.00
Average Score68.5%92.4%
Benchmarks
MMLU-Pro85.0%90.3%

Visual Benchmark Comparison

DeepSeek-V3.2 (Thinking)
Sakana Namazu
MMLU-Pro0.8 vs 0.9
0.8
0.9

Verdict

Sakana Namazu leads in 2 out of 4 comparison categories.

Overall Performance

Both models show comparable average scores: DeepSeek-V3.2 (Thinking) — 0.7, Sakana Namazu — 0.9.

API Cost

DeepSeek-V3.2 (Thinking) is 7.1x cheaper: input $0.28/1M vs $0.95/1M tokens.

Context Window

Sakana Namazu supports a larger context: 256K vs 131K tokens.

Recency

Sakana Namazu is newer: released 8/3/2026 vs 11/30/2025.

More About These Models

Related Comparisons

Frequently Asked Questions

Which is better for coding — DeepSeek-V3.2 (Thinking) 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 — DeepSeek-V3.2 (Thinking) or Sakana Namazu?
DeepSeek-V3.2 (Thinking) is cheaper for input: $0.28 per 1M tokens vs $0.95.
Which has a larger context window — DeepSeek-V3.2 (Thinking) or Sakana Namazu?
Sakana Namazu supports a larger context: 256,000 tokens vs 131,072.

The DeepSeek-V3.2 (Thinking) 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 DeepSeek-V3.2 (Thinking) or Sakana Namazu page. See also the complete list of AI model comparisons.