Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.5V (Non-reasoning) | Mistral Medium 3.1 |
|---|---|---|
| Input ($/M tokens) | $0.6 | $0.4 |
| Output ($/M tokens) | $1.8 | $2 |
Verdict. Mistral Medium 3.1 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 0.9× the per-million-token cost, GLM-4.5V (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GLM-4.5V (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.5V (Non-reasoning) is strongest on Intelligence Index (6.8). Mistral Medium 3.1 leads on Coding Index (20.5), Intelligence Index (14.7).
Speed. Throughput is comparable — 92 tok/s vs 106 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
Provider. Z AI and Mistral sell to overlapping but distinct developer audiences: Z AI 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): GLM-4.5V (Non-reasoning) costs $45.00 ($540/year); Mistral Medium 3.1 costs $42.00 ($504/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.5V (Non-reasoning) ≈ $6.60/run, Mistral Medium 3.1 ≈ $6.00/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.5V (Non-reasoning) ≈ $300/run, Mistral Medium 3.1 ≈ $280/run. Mistral Medium 3.1 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
Mistral Medium 3.1 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.80/M tokens vs $0.90/M for GLM-4.5V (Non-reasoning).
Mistral Medium 3.1 wins 2 out of 12 benchmarks compared to 0 for GLM-4.5V (Non-reasoning). See the detailed benchmark chart above for per-category results.
Mistral Medium 3.1 generates tokens faster at 106 tok/s vs 92 tok/s. However, Mistral Medium 3.1 has lower time-to-first-token (1.47s vs 1.85s).
Choose based on your priorities: Mistral Medium 3.1 for lower cost, Mistral Medium 3.1 for stronger benchmark performance, and Mistral Medium 3.1 for faster generation. For latency-sensitive apps, check the TTFT comparison above.