As of August 20, 2026, Sakana Namazu is 51% cheaper per million tokens than GPT-4.1. GPT-4.1 is $2.00 / $8.00 per million input/output tokens versus Sakana Namazu at $0.95 / $4.00. Sources: https://developers.openai.com/api/docs/pricing and https://sakana.ai
Sources: OpenAI API pricing (https://developers.openai.com/api/docs/pricing); Sakana AI pricing (https://sakana.ai)
GPT-4.1
OpenAI
Input
$2.00
Output
$8.00
Sakana Namazu
Sakana AI
Input
$0.95
Output
$4.00
Token pricing, specs, and benchmarks side by side
GPT-4.1Sakana NamazuVerdict
Sakana Namazu offers both lower pricing and stronger benchmark performance across the board, making it the clear value leader in this comparison.
As of August 20, 2026, Sakana Namazu is 51% cheaper on blended LLM API pricing. Sakana Namazu costs $0.95 per million input tokens and $4.00 per million output tokens ($4.95 blended 1M-in + 1M-out). GPT-4.1 costs $2.00 / $8.00 per million tokens ($10.00 blended). Sources: https://developers.openai.com/api/docs/pricing and https://sakana.ai. The cheapest LLM for your app still depends on how many output tokens you generate.
GPT-4.1 OpenAI API pricing is $2.00 input and $8.00 output per million tokens via OpenAI. Sakana Namazu Sakana AI API pricing is $0.95 input and $4.00 output per million tokens via Sakana AI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
GPT-4.1 has a published SWE-bench Verified score of 54.6%. Sakana Namazu does not have that eval on this page yet.
GPT-4.1 supports a 1M token context window, while Sakana Namazu supports 256K tokens. GPT-4.1 offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Sakana Namazu leads on 1 of 1 shared benchmarks versus GPT-4.1's 0 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both GPT-4.1 and Sakana Namazu are production API models. For cost-sensitive production traffic, Sakana Namazu has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call GPT-4.1 through OpenAI and Sakana Namazu through Sakana AI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published OpenAI API pricing and Sakana AI API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://sakana.ai. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
GPT-4.1 supports up to 33K output tokens per request, while Sakana Namazu supports up to 256K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
GPT-4.1 has a measured throughput of approximately 100 tokens/second. Throughput data for Sakana Namazu is not yet available. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-4.1 or Sakana Namazu from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. GPT-4.1 costs $2.00/M input and $8.00/M output. Sakana Namazu costs $0.95/M input and $4.00/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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