Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Kimi K2 | Qwen3 235B A22B 2507 (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.57 | $0.23 |
| Output ($/M tokens) | $2.3 | $2.3 |
Verdict. Qwen3 235B A22B 2507 (Reasoning) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen3 235B A22B 2507 (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 235B A22B 2507 (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Kimi K2 is strongest on Intelligence Index (19.7). Qwen3 235B A22B 2507 (Reasoning) leads on Coding Index (22.1), Intelligence Index (19.9).
Speed. On throughput, Qwen3 235B A22B 2507 (Reasoning) generates tokens at 64 tok/s versus 41 tok/s — about 36% faster. On time-to-first-token, Kimi K2 responds in 1220ms vs 2690ms, which matters most for chat-style UIs.
Provider. Kimi and Alibaba sell to overlapping but distinct developer audiences: Kimi tends to ship frontier reasoning models with premium positioning, while Alibaba 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 costs $51.60 ($619/year); Qwen3 235B A22B 2507 (Reasoning) costs $41.40 ($497/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Kimi K2 ≈ $7.45/run, Qwen3 235B A22B 2507 (Reasoning) ≈ $5.75/run. At agent/realtime scale (200M input / 100M output per million requests): Kimi K2 ≈ $344/run, Qwen3 235B A22B 2507 (Reasoning) ≈ $276/run. Qwen3 235B A22B 2507 (Reasoning) 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
Qwen3 235B A22B 2507 (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.75/M tokens vs $1.00/M for Kimi K2.
Qwen3 235B A22B 2507 (Reasoning) wins 2 out of 12 benchmarks compared to 0 for Kimi K2. See the detailed benchmark chart above for per-category results.
Qwen3 235B A22B 2507 (Reasoning) generates tokens faster at 64 tok/s vs 41 tok/s. Kimi K2 also has lower time-to-first-token (1.22s vs 2.69s).
Choose based on your priorities: Qwen3 235B A22B 2507 (Reasoning) for lower cost, Qwen3 235B A22B 2507 (Reasoning) for stronger benchmark performance, and Qwen3 235B A22B 2507 (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.