Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3.5 9B (Non-reasoning) | Mistral 7B Instruct |
|---|---|---|
| Input ($/M tokens) | $0.17 | $0.25 |
| Output ($/M tokens) | $0.25 | $0.25 |
Verdict. Qwen3.5 9B (Non-reasoning) wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Mistral 7B Instruct is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral 7B Instruct makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3.5 9B (Non-reasoning) is strongest on Coding Index (23.5), Intelligence Index (20.6). Mistral 7B Instruct leads on Intelligence Index (1.7).
Speed. On throughput, Mistral 7B Instruct generates tokens at 122 tok/s versus 87 tok/s — about 28% faster. On time-to-first-token, Mistral 7B Instruct responds in 760ms vs 850ms, which matters most for chat-style UIs.
Provider. Alibaba and Mistral sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Mistral 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): Qwen3.5 9B (Non-reasoning) costs $8.85 ($106/year); Mistral 7B Instruct costs $11.25 ($135/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3.5 9B (Non-reasoning) ≈ $1.35/run, Mistral 7B Instruct ≈ $1.75/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3.5 9B (Non-reasoning) ≈ $59/run, Mistral 7B Instruct ≈ $75/run. Qwen3.5 9B (Non-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.
Data from Artificial Analysis API — 12 benchmarks
Qwen3.5 9B (Non-reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.19/M tokens vs $0.25/M for Mistral 7B Instruct.
Qwen3.5 9B (Non-reasoning) wins 2 out of 12 benchmarks compared to 0 for Mistral 7B Instruct. See the detailed benchmark chart above for per-category results.
Mistral 7B Instruct generates tokens faster at 122 tok/s vs 87 tok/s. However, Mistral 7B Instruct has lower time-to-first-token (0.76s vs 0.85s).
Choose based on your priorities: Qwen3.5 9B (Non-reasoning) for lower cost, Qwen3.5 9B (Non-reasoning) for stronger benchmark performance, and Mistral 7B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.