As of August 20, 2026, U2 is 91% cheaper per million tokens than Sakana Namazu. Sakana Namazu is $0.95 / $4.00 per million input/output tokens versus U2 at $0.15 / $0.30. Sources: https://sakana.ai and https://anotherwrapper.com/tools/llm-pricing
Sources: Sakana AI pricing (https://sakana.ai); AnotherWrapper LLM pricing (https://anotherwrapper.com/tools/llm-pricing)
Sakana Namazu
Sakana AI
Input
$0.95
Output
$4.00
U2
Unisound
Input
$0.15
Output
$0.30
Token pricing, specs, and benchmarks side by side
Sakana NamazuU2Verdict
Both models are closely matched on benchmarks. U2 has the pricing advantage, making it the better value unless Sakana Namazu's specific capabilities are critical to your workflow.
As of August 20, 2026, U2 is 91% cheaper on blended LLM API pricing. U2 costs $0.15 per million input tokens and $0.30 per million output tokens ($0.45 blended 1M-in + 1M-out). Sakana Namazu costs $0.95 / $4.00 per million tokens ($4.95 blended). Sources: https://sakana.ai and https://anotherwrapper.com/tools/llm-pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
Sakana Namazu Sakana AI API pricing is $0.95 input and $4.00 output per million tokens via Sakana AI. U2 Unisound API pricing is $0.15 input and $0.30 output per million tokens via Unisound. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Neither Sakana Namazu nor U2 has a published SWE-bench or LiveCodeBench score on this page yet. Use the comparison table for the evals that do exist.
Sakana Namazu supports a 256K token context window, while U2 supports tokens. Sakana Namazu 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.
Shared benchmark scores for Sakana Namazu and U2 are still limited. Check the comparison table for the evals each model has published. 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 Sakana Namazu and U2 are production API models. For cost-sensitive production traffic, U2 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 Sakana Namazu through Sakana AI and U2 through Unisound 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 Sakana AI API pricing and Unisound API pricing, quoted per million tokens. Official rate sheets: https://sakana.ai and https://anotherwrapper.com/tools/llm-pricing. 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.
Sakana Namazu supports up to 256K output tokens per request, while U2 supports up to an unspecified number of output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Neither Sakana Namazu nor U2 has published throughput data on this page yet. 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 Sakana Namazu or U2 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. Sakana Namazu costs $0.95/M input and $4.00/M output. U2 costs $0.15/M input and $0.30/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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