Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Kimi K2.7 Code | DeepSeek R1 0528 (May '25) |
|---|---|---|
| Input ($/M tokens) | $0.95 | $1.35 |
| Output ($/M tokens) | $4 | $4.2 |
Verdict. Kimi K2.7 Code wins the overall benchmark matchup 7–4 across 11 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Kimi K2.7 Code is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Kimi K2.7 Code makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Kimi K2.7 Code is strongest on GPQA Diamond (90%), IFBench (63%), Coding Index (60.8). DeepSeek R1 0528 (May '25) leads on MATH-500 (98%), GPQA Diamond (81%), LiveCodeBench (77%).
Speed. On throughput, Kimi K2.7 Code generates tokens at 39 tok/s versus 0 tok/s — about 100% faster. On time-to-first-token, DeepSeek R1 0528 (May '25) responds in 0ms vs 1178ms, which matters most for chat-style UIs.
Provider. Kimi and DeepSeek sell to overlapping but distinct developer audiences: Kimi tends to ship frontier reasoning models with premium positioning, while DeepSeek often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Kimi K2.7 Code costs $88.50 ($1062/year); DeepSeek R1 0528 (May '25) costs $103.50 ($1242/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Kimi K2.7 Code ≈ $12.75/run, DeepSeek R1 0528 (May '25) ≈ $15.15/run. At agent/realtime scale (200M input / 100M output per million requests): Kimi K2.7 Code ≈ $590/run, DeepSeek R1 0528 (May '25) ≈ $690/run. Kimi K2.7 Code becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Kimi K2.7 Code is cheaper overall. Its blended price (3:1 input/output ratio) is $1.71/M tokens vs $2.06/M for DeepSeek R1 0528 (May '25).
Kimi K2.7 Code wins 7 out of 12 benchmarks compared to 4 for DeepSeek R1 0528 (May '25). See the detailed benchmark chart above for per-category results.
Kimi K2.7 Code generates tokens faster at 39 tok/s vs 0 tok/s. However, DeepSeek R1 0528 (May '25) has lower time-to-first-token (0.00s vs 1.18s).
Choose based on your priorities: Kimi K2.7 Code for lower cost, Kimi K2.7 Code for stronger benchmark performance, and Kimi K2.7 Code for faster generation. For latency-sensitive apps, check the TTFT comparison above.