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Parse vs Sakana Namazu

Comparing Parse and Sakana Namazu across benchmarks, pricing, and capabilities.

Cohere · Sakana AI · Updated for 2026

Which is better?

Parse and Sakana Namazu trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Sakana Namazu also accepts a larger context window (256,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Parse

  • you want the most recent training data — it shipped Aug 2026

Choose Sakana Namazu

  • you process long inputs — it offers a 256,000 token context window

At a glance

The differences that matter most.

Benchmark wins
Input price
— / M
$0.95 / M
Output price
— / M
$4.00 / M
Context window
8,192
256,000

Individual benchmarks

1 reported for Parse · 3 for Sakana Namazu

No common benchmarks found

Parse and Sakana Namazudon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Sakana Namazu accepts 256,000 input tokens compared to Parse's 8,192 tokens. Only Sakana Namazu specifies output context (256,000 tokens).

Cohere
Parse
Input8,192 tokens
Output- tokens
Sakana AI
Sakana Namazu
Input256,000 tokens
Output256,000 tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Parse and Sakana Namazu support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Parse

Text
Images
Audio
Video

Sakana Namazu

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Parse

Proprietary

Closed source

Sakana Namazu

Proprietary

Closed source

Release Timeline

When each model was launched

Parse was released on 2026-08-27, while Sakana Namazu was released on 2026-08-03.

Parse is 1 month newer than Sakana Namazu.

Parse

Aug 27, 2026

3 weeks ago

3w newer
Sakana Namazu

Aug 3, 2026

1 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Parse is available from Azure, Cohere. Sakana Namazu is available from Sakana AI.

Parse

azure logo
Azure
cohere logo
Cohere

Sakana Namazu

sakana logo
Unknown Organization
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Parse and Sakana Namazu side-by-side, then vote on the output you prefer.

Parse
✓ Preferred
Sakana Namazu
Open in Playground

FAQ

Common questions about Parse vs Sakana Namazu.

Which is better, Parse or Sakana Namazu?

Parse (Cohere) and Sakana Namazu (Sakana AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Parse compare to Sakana Namazu in benchmarks?

Parse scores ParseBench: 79.2%. Sakana Namazu scores AIME 2026: 96.7%, LiveCodeBench v6: 90.3%, MMLU-Pro: 90.3%.

What are the context window sizes for Parse and Sakana Namazu?

Parse supports 8K tokens and Sakana Namazu supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Parse and Sakana Namazu?

Key differences include context window (8K vs 256K). See the full comparison above for benchmark-by-benchmark results.

Who makes Parse and Sakana Namazu?

Parse is developed by Cohere and Sakana Namazu is developed by Sakana AI.