Qwen3-Coder 480B-A35B Instruct vs GLM-4.5V vs Pixtral Large (25.02)
GLM-4.5V comes out ahead, 49 to 33 and 28 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3-Coder 480B-A35B Instruct
28/100- ECI—
- Price$1.50 / $7.50
- Context262K
- Our pick
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against Pixtral Large (25.02) (33) and Qwen3-Coder 480B-A35B Instruct (28). It leads on price and inputs & features. Qwen3-Coder 480B-A35B Instruct 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 priceGLM-4.5VGLM-4.5V $0.90 · Qwen3-Coder 480B-A35B Instruct $3.00 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3-Coder 480B-A35B InstructQwen3-Coder 480B-A35B Instruct 262,144 · Pixtral Large (25.02) 128,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VQwen3-Coder 480B-A35B Instruct: Text · GLM-4.5V: Text, Images, Video · Pixtral Large (25.02): Text, Images
- Self-hostingQwen3-Coder 480B-A35B Instruct and GLM-4.5VPublishes downloadable weights
| Measure | Weight | Qwen3-Coder 480B-A35B Instruct | GLM-4.5V | Pixtral Large (25.02) |
|---|---|---|---|---|
| Price | 50% | 27 | 52 | 27 |
| Inputs & features | 30% | 25 | 70 | 50 |
| Context window | 20% | 37 | 12 | 24 |
| Overall | 100% | 28/100 | 49/100 | 33/100 |
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 | Qwen3-Coder 480B-A35B InstructAlibaba (Qwen) | GLM-4.5VZ.ai (Zhipu) | Pixtral Large (25.02)Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.50 | $0.60 (best) | $2.00 |
| Output | $7.50 | $1.80 (best) | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.90 (best) | $3.00 |
| Long-context rate | Over 32K: $2.70 / $13.50 | Same rate | Same rate |
| Price source | Official Alibaba API | Official Z.AI API | Median of 3 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 64,000 tokens | 128,000 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | qwen3-coder-480b-a35b-instruct | glm-4.5v | — |
| API providers | 7 | 11 (best) | 3 |
| Released | Apr 2025 | Aug 11, 2025 | Apr 8, 2025 |
| Knowledge cutoff | Apr 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.
- Qwen3-Coder 480B-A35B Instruct$30.00
- GLM-4.5V$9.60
- Pixtral Large (25.02)$32.00
Which should you choose?
Which is better: Qwen3-Coder 480B-A35B Instruct, GLM-4.5V or Pixtral Large (25.02)?
GLM-4.5V is the better all-round choice, scoring 49/100 against Pixtral Large (25.02) (33) and Qwen3-Coder 480B-A35B Instruct (28). It leads on price and inputs & features. Qwen3-Coder 480B-A35B Instruct 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, Qwen3-Coder 480B-A35B Instruct, GLM-4.5V or Pixtral Large (25.02)?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Qwen3-Coder 480B-A35B Instruct costs $1.50 input / $7.50 output per million tokens (official Alibaba API price); Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $3.00 for Qwen3-Coder 480B-A35B Instruct (3.3× as much) and $3.00 for Pixtral Large (25.02) (3.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3-Coder 480B-A35B Instruct has not been scored yet, GLM-4.5V has not been scored yet and Pixtral Large (25.02) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3-Coder 480B-A35B Instruct, GLM-4.5V and Pixtral Large (25.02) 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?
Qwen3-Coder 480B-A35B Instruct has the largest context window at 262,144 tokens, against 128,000 for Pixtral Large (25.02) and 64,000 for GLM-4.5V. Maximum output per response: Qwen3-Coder 480B-A35B Instruct up to 65,536, GLM-4.5V up to 16,384, Pixtral Large (25.02) up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen3-Coder 480B-A35B Instruct accepts text; GLM-4.5V accepts text, images and video; Pixtral Large (25.02) accepts text and images. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
Qwen3-Coder 480B-A35B Instruct and GLM-4.5V publishes its weights and can be self-hosted; Pixtral Large (25.02) is proprietary.
Which is newer?
GLM-4.5V is the newest, released Aug 11, 2025. Pixtral Large (25.02) came out Apr 8, 2025; Qwen3-Coder 480B-A35B Instruct came out Apr 2025. Knowledge cutoff: Qwen3-Coder 480B-A35B Instruct Apr 2025, GLM-4.5V 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.
