GLM-4.5V vs Mistral Medium 3.1 vs Qwen3 Max
Too close to call on our weighted score (Mistral Medium 3.1 50, GLM-4.5V 49, Qwen3 Max 31). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Mistral AIMistral Medium 3.1
50/100- ECI—
- Price$0.40 / $2.00
- Context262K
Alibaba (Qwen)Qwen3 Max
31/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Medium 3.1 50/100, GLM-4.5V 49/100, Qwen3 Max 31/100), so choose by what matters most for your work: Mistral Medium 3.1 on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMistral Medium 3.1Mistral Medium 3.1 $0.80 · GLM-4.5V $0.90 · Qwen3 Max $2.40 per 1M tokens (3:1 blend)
- Longest contextMistral Medium 3.1 and Qwen3 MaxMistral Medium 3.1 262,144 · Qwen3 Max 262,144 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Mistral Medium 3.1: Text, Images · Qwen3 Max: Text
- Self-hostingGLM-4.5VPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Mistral Medium 3.1 | Qwen3 Max |
|---|---|---|---|---|
| Price | 50% | 52 | 54 | 32 |
| Inputs & features | 30% | 70 | 50 | 25 |
| Context window | 20% | 12 | 37 | 37 |
| Overall | 100% | 49/100 | 50/100 | 31/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.5VZ.ai (Zhipu) | Mistral Medium 3.1Mistral AI | Qwen3 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 142.4 |
| ECI rank | — | — | #91 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 72.6% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 19.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 73.3% |
| SimpleQA VerifiedShort factual questions | — | — | 48.8% |
| Price per million tokens | |||
| Input | $0.60 | $0.40 (best) | $1.20 |
| Output | $1.80 (best) | $2.00 | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.80 (best) | $2.40 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 16,384 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | glm-4.5v | mistral-medium-2508 | qwen3-max |
| API providers | 11 | 1 | 16 (best) |
| Released | Aug 11, 2025 | Aug 12, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Apr 2025 | May 2025 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.5V$9.60
- Mistral Medium 3.1$8.00
- Qwen3 Max$24.00
Which should you choose?
Which is better: GLM-4.5V, Mistral Medium 3.1 or Qwen3 Max?
It is close. Our weighted score puts them within a point (Mistral Medium 3.1 50/100, GLM-4.5V 49/100, Qwen3 Max 31/100), so choose by what matters most for your work: Mistral Medium 3.1 on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-4.5V, Mistral Medium 3.1 or Qwen3 Max?
Mistral Medium 3.1 is cheaper at $0.40 input / $2.00 output per million tokens (official Mistral API price). GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price); Qwen3 Max costs $1.20 input / $6.00 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.80 per million tokens for Mistral Medium 3.1 versus $0.90 for GLM-4.5V (1.1× as much) and $2.40 for Qwen3 Max (3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Mistral Medium 3.1 has not been scored yet and Qwen3 Max has an ECI of 142.4.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Mistral Medium 3.1 and Qwen3 Max yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Mistral Medium 3.1 and Qwen3 Max have the largest context windows (262,144 and 262,144 tokens), against 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Mistral Medium 3.1 up to 262,144, Qwen3 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Mistral Medium 3.1 accepts text and images; Qwen3 Max accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V publishes its weights and can be self-hosted; Mistral Medium 3.1 and Qwen3 Max is proprietary.
Which is newer?
Qwen3 Max is the newest, released Sep 23, 2025. Mistral Medium 3.1 came out Aug 12, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Mistral Medium 3.1 May 2025, Qwen3 Max Apr 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
