Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Mistral 7B Instruct | Qwen3.5 9B (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.25 | $0.17 |
| Output ($/M tokens) | $0.25 | $0.25 |
Verdict. Qwen3.5 9B (Non-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, Qwen3.5 9B (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.5 9B (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Mistral 7B Instruct is strongest on Intelligence Index (5.0). Qwen3.5 9B (Non-reasoning) leads on Coding Index (23.5), Intelligence Index (13.3).
Speed. Throughput is comparable — 84 tok/s vs 93 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
Provider. Mistral and Alibaba sell to overlapping but distinct developer audiences: Mistral 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): Mistral 7B Instruct costs $11.25 ($135/year); Qwen3.5 9B (Non-reasoning) costs $8.85 ($106/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral 7B Instruct ≈ $1.75/run, Qwen3.5 9B (Non-reasoning) ≈ $1.35/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral 7B Instruct ≈ $75/run, Qwen3.5 9B (Non-reasoning) ≈ $59/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.
Qwen3.5 9B (Non-reasoning) generates tokens faster at 93 tok/s vs 84 tok/s. Mistral 7B Instruct also has lower time-to-first-token (0.74s vs 0.77s).
Choose based on your priorities: Qwen3.5 9B (Non-reasoning) for lower cost, Qwen3.5 9B (Non-reasoning) for stronger benchmark performance, and Qwen3.5 9B (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.