Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 VL 235B A22B Instruct | Mistral Large 3 |
|---|---|---|
| Input ($/M tokens) | $0.4 | $0.5 |
| Output ($/M tokens) | $1.6 | $1.5 |
Verdict. Mistral Large 3 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 1.1× the per-million-token cost, Mistral Large 3 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Large 3 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 VL 235B A22B Instruct is strongest on Intelligence Index (14.4). Mistral Large 3 leads on Coding Index (20.1), Intelligence Index (15.9).
Speed. On throughput, Mistral Large 3 generates tokens at 78 tok/s versus 51 tok/s — about 35% faster. On time-to-first-token, Mistral Large 3 responds in 1110ms vs 2740ms, 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 VL 235B A22B Instruct costs $36.00 ($432/year); Mistral Large 3 costs $37.50 ($450/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 VL 235B A22B Instruct ≈ $5.20/run, Mistral Large 3 ≈ $5.50/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 VL 235B A22B Instruct ≈ $240/run, Mistral Large 3 ≈ $250/run. Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.70/M tokens vs $0.75/M for Mistral Large 3.
Mistral Large 3 wins 2 out of 12 benchmarks compared to 0 for Qwen3 VL 235B A22B Instruct. See the detailed benchmark chart above for per-category results.
Mistral Large 3 generates tokens faster at 78 tok/s vs 51 tok/s. However, Mistral Large 3 has lower time-to-first-token (1.11s vs 2.74s).
Choose based on your priorities: Qwen3 VL 235B A22B Instruct for lower cost, Mistral Large 3 for stronger benchmark performance, and Mistral Large 3 for faster generation. For latency-sensitive apps, check the TTFT comparison above.