GLM-4.5-Flash vs Magistral Medium vs Qwen3-Coder 480B-A35B Instruct
GLM-4.5-Flash comes out ahead, 65 to 30 and 28 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
Mistral AIMagistral Medium
30/100- ECI—
- Price$2.00 / $5.00
- Context128K
Alibaba (Qwen)Qwen3-Coder 480B-A35B Instruct
28/100- ECI—
- Price$1.50 / $7.50
- Context262K
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Magistral Medium (30) and Qwen3-Coder 480B-A35B Instruct (28). It leads on price. 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-4.5-FlashGLM-4.5-Flash Free · Magistral Medium $2.75 · Qwen3-Coder 480B-A35B Instruct $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3-Coder 480B-A35B InstructQwen3-Coder 480B-A35B Instruct 262,144 · GLM-4.5-Flash 131,072 · Magistral Medium 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Flash: Text · Magistral Medium: Text · Qwen3-Coder 480B-A35B Instruct: Text
- Self-hostingQwen3-Coder 480B-A35B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Magistral Medium | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 29 | 27 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 24 | 24 | 37 |
| Overall | 100% | 65/100 | 30/100 | 28/100 |
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-4.5-FlashZ.ai (Zhipu) | Magistral MediumMistral AI | Qwen3-Coder 480B-A35B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | $2.00 | $1.50 |
| Output | Free (best) | $5.00 | $7.50 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $2.75 | $3.00 |
| Long-context rate | Same rate | Same rate | Over 32K: $2.70 / $13.50 |
| Price source | Official Z.AI API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 262,144 tokens (best) |
| Max output | 98,304 tokens (best) | 16,384 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | glm-4.5-flash | magistral-medium-latest | qwen3-coder-480b-a35b-instruct |
| API providers | 4 | 4 | 7 (best) |
| Released | Jul 28, 2025 | Mar 17, 2025 | Apr 2025 |
| Knowledge cutoff | Apr 2025 | Jun 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-4.5-FlashFree
- Magistral Medium$30.00
- Qwen3-Coder 480B-A35B Instruct$30.00
Which should you choose?
Which is better: GLM-4.5-Flash, Magistral Medium or Qwen3-Coder 480B-A35B Instruct?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Magistral Medium (30) and Qwen3-Coder 480B-A35B Instruct (28). It leads on price. 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, GLM-4.5-Flash, Magistral Medium or Qwen3-Coder 480B-A35B Instruct?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Magistral Medium costs $2.00 input / $5.00 output per million tokens (official Mistral API price); Qwen3-Coder 480B-A35B Instruct costs $1.50 input / $7.50 output per million tokens (official Alibaba API price). GLM-4.5-Flash is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5-Flash has not been scored yet, Magistral Medium has not been scored yet and Qwen3-Coder 480B-A35B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Magistral Medium and Qwen3-Coder 480B-A35B 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?
Qwen3-Coder 480B-A35B Instruct has the largest context window at 262,144 tokens, against 131,072 for GLM-4.5-Flash and 128,000 for Magistral Medium. Maximum output per response: GLM-4.5-Flash up to 98,304, Magistral Medium up to 16,384, Qwen3-Coder 480B-A35B Instruct up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Magistral Medium accepts text; Qwen3-Coder 480B-A35B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Qwen3-Coder 480B-A35B Instruct publishes its weights and can be self-hosted; GLM-4.5-Flash and Magistral Medium is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Qwen3-Coder 480B-A35B Instruct came out Apr 2025; Magistral Medium came out Mar 17, 2025. Knowledge cutoff: GLM-4.5-Flash Apr 2025, Magistral Medium Jun 2025, Qwen3-Coder 480B-A35B 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.
