Which is cheaper, DeepSeek-V4-Flash-0423 or Kimi K2.7 Code?
As of August 20, 2026, DeepSeek-V4-Flash-0423 is 94% cheaper on blended LLM API pricing. DeepSeek-V4-Flash-0423 costs $0.10 per million input tokens and $0.20 per million output tokens ($0.30 blended 1M-in + 1M-out). Kimi K2.7 Code costs $0.96 / $3.97 per million tokens ($4.93 blended). Sources: https://api-docs.deepseek.com/quick_start/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 DeepSeek-V4-Flash-0423 vs Kimi K2.7 Code cost per million tokens?
DeepSeek-V4-Flash-0423 DeepSeek API pricing is $0.10 input and $0.20 output per million tokens via DeepSeek. Kimi K2.7 Code Moonshot AI API pricing is $0.96 input and $3.97 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, DeepSeek-V4-Flash-0423 or Kimi K2.7 Code?
DeepSeek-V4-Flash-0423 leads on SWE-bench Verified: DeepSeek-V4-Flash-0423 at 78.6% vs Kimi K2.7 Code at 78.2%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for DeepSeek-V4-Flash-0423 vs Kimi K2.7 Code?
DeepSeek-V4-Flash-0423 supports a 1M token context window, while Kimi K2.7 Code supports 262K tokens. DeepSeek-V4-Flash-0423 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, DeepSeek-V4-Flash-0423 or Kimi K2.7 Code?
DeepSeek-V4-Flash-0423 and Kimi K2.7 Code are tied across 4 shared benchmarks. Check the table for category-level strengths. 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 DeepSeek-V4-Flash-0423 or Kimi K2.7 Code better for production use?
Both DeepSeek-V4-Flash-0423 and Kimi K2.7 Code are production API models. For cost-sensitive production traffic, DeepSeek-V4-Flash-0423 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 DeepSeek-V4-Flash-0423 and Kimi K2.7 Code in my app?
Yes. Call DeepSeek-V4-Flash-0423 through DeepSeek and Kimi K2.7 Code 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 DeepSeek-V4-Flash-0423 vs Kimi K2.7 Code pricing data?
List rates are the latest published DeepSeek API pricing and Moonshot AI API pricing, quoted per million tokens. Official rate sheets: https://api-docs.deepseek.com/quick_start/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 DeepSeek-V4-Flash-0423 and Kimi K2.7 Code?
DeepSeek-V4-Flash-0423 supports up to 66K output tokens per request, while Kimi K2.7 Code supports up to 131K 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, DeepSeek-V4-Flash-0423 or Kimi K2.7 Code?
Neither DeepSeek-V4-Flash-0423 nor Kimi K2.7 Code 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 DeepSeek-V4-Flash-0423 vs Kimi K2.7 Code comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open DeepSeek-V4-Flash-0423 or Kimi K2.7 Code from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
