Which is cheaper, Kimi K2.7 Code or Magistral Small 2506?
As of August 30, 2026, Magistral Small 2506 is 59% cheaper on blended LLM API pricing. Magistral Small 2506 costs $0.50 per million input tokens and $1.50 per million output tokens ($2.00 blended 1M-in + 1M-out). Kimi K2.7 Code costs $0.96 / $3.97 per million tokens ($4.93 blended). Sources: https://platform.kimi.com/docs/pricing/chat and https://mistral.ai/technology/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Kimi K2.7 Code vs Magistral Small 2506 cost per million tokens?
Kimi K2.7 Code Moonshot AI API pricing is $0.96 input and $3.97 output per million tokens via Moonshot AI. Magistral Small 2506 Mistral API pricing is $0.50 input and $1.50 output per million tokens via Mistral AI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Kimi K2.7 Code or Magistral Small 2506?
Kimi K2.7 Code has a published SWE-bench Verified score of 78.2%. Magistral Small 2506 does not have that eval on this page yet.
What is the context window for Kimi K2.7 Code vs Magistral Small 2506?
Kimi K2.7 Code supports a 262K token context window, while Magistral Small 2506 supports 40K tokens. Kimi K2.7 Code 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, Kimi K2.7 Code or Magistral Small 2506?
Kimi K2.7 Code leads on 3 of 3 shared benchmarks versus Magistral Small 2506'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 Kimi K2.7 Code or Magistral Small 2506 better for production use?
Both Kimi K2.7 Code and Magistral Small 2506 are production API models. For cost-sensitive production traffic, Magistral Small 2506 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 Kimi K2.7 Code and Magistral Small 2506 in my app?
Yes. Call Kimi K2.7 Code through Moonshot AI and Magistral Small 2506 through Mistral 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 Kimi K2.7 Code vs Magistral Small 2506 pricing data?
List rates are the latest published Moonshot AI API pricing and Mistral API pricing, quoted per million tokens. Official rate sheets: https://platform.kimi.com/docs/pricing/chat and https://mistral.ai/technology/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.
What is the output token limit for Kimi K2.7 Code and Magistral Small 2506?
Kimi K2.7 Code supports up to 131K output tokens per request, while Magistral Small 2506 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.
Which model has better throughput, Kimi K2.7 Code or Magistral Small 2506?
Neither Kimi K2.7 Code nor Magistral Small 2506 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.
How do I switch the models in this Kimi K2.7 Code vs Magistral Small 2506 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Kimi K2.7 Code or Magistral Small 2506 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
