Standard Compute
Cost per coding-agent request

Is DeepSeek V4 Flash or Kimi K2.7 Code cheaper for coding agents?

For coding agents, Kimi K2.7 Code costs 5.2x as much as DeepSeek V4 Flash per request ($0.027 vs $0.0052 on our measured traffic), because Kimi K2.7 Code lists at $0.671 input, $0.18 cached input and $3.35 output per million tokens against $0.30, $0.006 and $1.20 for DeepSeek V4 Flash.

List prices: DeepSeek API pricing, OpenRouter listed price, checked 2026-10-13. Request costs: Standard Compute gateway data, 79,301 coding-agent requests from 75 users, Sep 13 to Oct 12, 2026. How we compute this

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Cost per coding-agent request, side by side

Cost at list priceDeepSeek V4 FlashKimi K2.7 Code
Prompt under 100K tokens50% of requests, 87% of input cached$0.0033$0.016
Prompt of 100K to 272K39% of requests, 88% of input cached$0.0078$0.042
Prompt over 272K tokens11% of requests, 86% of input cachedn/an/a
Average request$0.0052$0.027
100 requests$0.52$2.71
Median session16 requests; a session ends after 10 idle minutes$0.066$0.31
A month of regular usemedian of 20 users active on 5+ days$6.20$35.08

Each logged request priced at the listed rates above for its own token counts, including cache reads and any higher rate for long prompts. Average output: 698 tokens per request. Both models are priced on the requests up to 262K tokens that fit in both context windows; band shares above describe all requests.

Benchmarks, speed and latency, side by side

Artificial AnalysisDeepSeek V4 FlashKimi K2.7 Code
Intelligence Index10 evaluations, 0 to 1003926
Terminal-Bench 4.0agentic coding in a terminal26.8%1.0%
Output speedmedian195 tokens/s75 tokens/s
Time to first answer tokenincludes thinking time11 s33 s

Source: Artificial Analysis (artificialanalysis.ai). Measured variants: DeepSeek V4.1 Flash (Max), speed on DeepSeek; Kimi K2.7 Code, speed on Kimi. Fetched October 13, 2026. Artificial Analysis runs Terminal-Bench 4.0 in its own setup, so its scores differ from the tbench.ai leaderboard.

What to watch for

Kimi K2.7 Code's 262K window cannot take 12% of coding-agent requests, so the comparison uses only requests both can take. 87% of agent input is read from cache, and Kimi K2.7 Code charges $0.18 per million for it against $0.006. DeepSeek V4 Flash is superseded: DeepSeek V4.1 Flash costs $0.0067 per request.

FAQ

Is DeepSeek V4 Flash cheaper than Kimi K2.7 Code for coding agents?
Yes, at the listed prices. 100 average coding-agent requests cost $0.52 on DeepSeek V4 Flash and $2.71 on Kimi K2.7 Code. A median 16-request session costs $0.066 vs $0.31. Measured on 79,301 real coding-agent requests (Sep 13 to Oct 12, 2026), counting only the requests up to 262K tokens that both can take.
How much does a month of Kimi K2.7 Code vs DeepSeek V4 Flash cost?
For the median regular user in our data (1,478 requests in 30 days), about $35.08 on Kimi K2.7 Code and $6.20 on DeepSeek V4 Flash at list price. Heavier users pay proportionally more.
DeepSeek V4 Flash vs Kimi K2.7 Code: which is better for agents?
DeepSeek V4 Flash scores higher on output quality (7.7/10 vs 5.8/10), while DeepSeek V4 Flash is the cheaper option. DeepSeek V4 Flash is best for existing setups that still call the deepseek-v4-flash name; Kimi K2.7 Code is best for coding agents that need an open-weight model with a coding-specific tune. Live community votes on this page show what people actually prefer.
Can I switch between DeepSeek V4 Flash and Kimi K2.7 Code in the same agent?
Yes. Model-agnostic agents (Cline, Aider, OpenClaw, Hermes Agent, Kilo Code, OpenCode) let you swap models per task. Many people route hard tasks to the stronger model and routine work to the cheaper one.

Community vote

Pick a winner in each dimension. You can change your vote any time. 1 vote cast so far.

Output Quality

Correctness and depth of what it produces

0%1 vote100%

Agentic Ability

Tool calls, instruction-following, and multi-step tasks

Speed

Tokens per second and time-to-first-token

Value for Money

How much capability you get per dollar

Reliability

Consistent results: fewer refusals, loops and format breaks

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