GLM-4.5V vs Mistral Medium 3.1 vs Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct comes out ahead, 53 to 50 and 49 on our weighted score, and it is the cheaper option too.
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
- Our pick
Alibaba (Qwen)Qwen3 VL 235B A22B Instruct
53/100- ECI—
- Price$0.30 / $1.55
- Context131K
Qwen3 VL 235B A22B Instruct is our pick
Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against Mistral Medium 3.1 (50) and GLM-4.5V (49). It leads on price. GLM-4.5V wins on inputs & features. Mistral Medium 3.1 wins on context window. 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 priceQwen3 VL 235B A22B InstructQwen3 VL 235B A22B Instruct $0.613 · Mistral Medium 3.1 $0.80 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
- Longest contextMistral Medium 3.1Mistral Medium 3.1 262,144 · Qwen3 VL 235B A22B Instruct 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Mistral Medium 3.1: Text, Images · Qwen3 VL 235B A22B Instruct: Text, Images
- Self-hostingGLM-4.5V and Qwen3 VL 235B A22B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Mistral Medium 3.1 | Qwen3 VL 235B A22B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 54 | 60 |
| Inputs & features | 30% | 70 | 50 | 60 |
| Context window | 20% | 12 | 37 | 24 |
| Overall | 100% | 49/100 | 50/100 | 53/100 |
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 VL 235B A22B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.40 | $0.30 (best) |
| Output | $1.80 | $2.00 | $1.55 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.80 | $0.613 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Mistral API | Median of 12 providers |
| Limits | |||
| Context window | 64,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 262,144 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| 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 | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.5v | mistral-medium-2508 | — |
| API providers | 11 | 1 | 12 (best) |
| Released | Aug 11, 2025 | Aug 12, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Apr 2025 | May 2025 | Mar 31, 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 VL 235B A22B Instruct$6.10
Which should you choose?
Which is better: GLM-4.5V, Mistral Medium 3.1 or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct is the better all-round choice, scoring 53/100 against Mistral Medium 3.1 (50) and GLM-4.5V (49). It leads on price. GLM-4.5V wins on inputs & features. Mistral Medium 3.1 wins on context window. 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 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct is cheaper at $0.30 input / $1.55 output per million tokens (median across 12 API providers). Mistral Medium 3.1 costs $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). At a typical mix of three input tokens to one output token, that is $0.613 per million tokens for Qwen3 VL 235B A22B Instruct versus $0.80 for Mistral Medium 3.1 (1.3× as much) and $0.90 for GLM-4.5V (1.5× 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 VL 235B A22B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Mistral Medium 3.1 and Qwen3 VL 235B A22B Instruct 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 has the largest context window at 262,144 tokens, against 131,072 for Qwen3 VL 235B A22B Instruct and 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 VL 235B A22B Instruct up to 32,768 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 VL 235B A22B Instruct accepts text and images. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V and Qwen3 VL 235B A22B Instruct publishes its weights and can be self-hosted; Mistral Medium 3.1 is proprietary.
Which is newer?
Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct Mar 31, 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.
