GLM-4.5V vs Mistral Medium 3.1 vs Qwen3-Coder 30B-A3B Instruct
Too close to call on our weighted score (Mistral Medium 3.1 50, GLM-4.5V 49, Qwen3-Coder 30B-A3B Instruct 41). 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-Coder 30B-A3B Instruct
41/100- ECI—
- Price$0.45 / $2.25
- 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-Coder 30B-A3B Instruct 41/100), 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-Coder 30B-A3B Instruct $0.90 per 1M tokens (3:1 blend)
- Longest contextMistral Medium 3.1 and Qwen3-Coder 30B-A3B InstructMistral Medium 3.1 262,144 · Qwen3-Coder 30B-A3B Instruct 262,144 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Mistral Medium 3.1: Text, Images · Qwen3-Coder 30B-A3B Instruct: Text
- Self-hostingGLM-4.5V and Qwen3-Coder 30B-A3B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Mistral Medium 3.1 | Qwen3-Coder 30B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 54 | 52 |
| Inputs & features | 30% | 70 | 50 | 25 |
| Context window | 20% | 12 | 37 | 37 |
| Overall | 100% | 49/100 | 50/100 | 41/100 |
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-Coder 30B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.40 (best) | $0.45 |
| Output | $1.80 (best) | $2.00 | $2.25 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.80 (best) | $0.90 |
| Long-context rate | Same rate | Same rate | Over 32K: $0.75 / $3.75 |
| 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 | Open |
| API model ID | glm-4.5v | mistral-medium-2508 | qwen3-coder-30b-a3b-instruct |
| API providers | 11 | 1 | 13 (best) |
| Released | Aug 11, 2025 | Aug 12, 2025 | Apr 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-Coder 30B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, Mistral Medium 3.1 or Qwen3-Coder 30B-A3B Instruct?
It is close. Our weighted score puts them within a point (Mistral Medium 3.1 50/100, GLM-4.5V 49/100, Qwen3-Coder 30B-A3B Instruct 41/100), 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-Coder 30B-A3B Instruct?
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-Coder 30B-A3B Instruct costs $0.45 input / $2.25 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 $0.90 for Qwen3-Coder 30B-A3B Instruct (1.1× 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-Coder 30B-A3B 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-Coder 30B-A3B 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 and Qwen3-Coder 30B-A3B Instruct 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-Coder 30B-A3B Instruct 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-Coder 30B-A3B Instruct accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V and Qwen3-Coder 30B-A3B Instruct publishes its weights and can be self-hosted; Mistral Medium 3.1 is proprietary.
Which is newer?
Mistral Medium 3.1 is the newest, released Aug 12, 2025. GLM-4.5V came out Aug 11, 2025; Qwen3-Coder 30B-A3B Instruct came out Apr 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Mistral Medium 3.1 May 2025, Qwen3-Coder 30B-A3B Instruct 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.
