Which is cheaper, Claude Opus 5 or Kimi K2 0905?
As of August 20, 2026, Kimi K2 0905 is 90% cheaper on blended LLM API pricing. Kimi K2 0905 costs $0.60 per million input tokens and $2.50 per million output tokens ($3.10 blended 1M-in + 1M-out). Claude Opus 5 costs $5.00 / $25.00 per million tokens ($30.00 blended). Sources: https://platform.claude.com/docs/en/about-claude/pricing and https://platform.kimi.com/docs/pricing/chat. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Claude Opus 5 vs Kimi K2 0905 cost per million tokens?
Claude Opus 5 Claude API pricing from Anthropic is $5.00 input and $25.00 output per million tokens via Anthropic. Kimi K2 0905 Moonshot AI API pricing is $0.60 input and $2.50 output per million tokens via Moonshot AI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Claude Opus 5 or Kimi K2 0905?
Claude Opus 5 has a published SWE-bench Verified score of 96%. Kimi K2 0905 does not have that eval on this page yet.
What is the context window for Claude Opus 5 vs Kimi K2 0905?
Claude Opus 5 supports a 1M token context window, while Kimi K2 0905 supports 262K tokens. Claude Opus 5 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.
Which model performs better on benchmarks, Claude Opus 5 or Kimi K2 0905?
Claude Opus 5 leads on 1 of 1 shared benchmarks versus Kimi K2 0905'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.
Is Claude Opus 5 or Kimi K2 0905 better for production use?
Both Claude Opus 5 and Kimi K2 0905 are production API models. For cost-sensitive production traffic, Kimi K2 0905 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.
Can I switch between Claude Opus 5 and Kimi K2 0905 in my app?
Yes. Call Claude Opus 5 through Anthropic and Kimi K2 0905 through Moonshot 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.
How accurate is this Claude Opus 5 vs Kimi K2 0905 pricing data?
List rates are the latest published Claude API pricing from Anthropic and Moonshot AI API pricing, quoted per million tokens. Official rate sheets: https://platform.claude.com/docs/en/about-claude/pricing and https://platform.kimi.com/docs/pricing/chat. 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.
What is the output token limit for Claude Opus 5 and Kimi K2 0905?
Claude Opus 5 supports up to 128K output tokens per request, while Kimi K2 0905 supports up to 262K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, Claude Opus 5 or Kimi K2 0905?
Claude Opus 5 has a measured throughput of approximately 3.437096666269855 tokens/second. Throughput data for Kimi K2 0905 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.
How do I switch the models in this Claude Opus 5 vs Kimi K2 0905 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Claude Opus 5 or Kimi K2 0905 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
