GLM-4.5-Flash vs Mistral Nemotron vs Qwen3-VL 30B-A3B
GLM-4.5-Flash comes out ahead, 65 to 62 and 59 on our weighted score.
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
NVIDIAMistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
Alibaba (Qwen)Qwen3-VL 30B-A3B
59/100- ECI—
- Price$0.20 / $0.80
- Context131K
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Qwen3-VL 30B-A3B (59). Qwen3-VL 30B-A3B wins on inputs & features. 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-Flash and Mistral NemotronGLM-4.5-Flash Free · Mistral Nemotron Free · Qwen3-VL 30B-A3B $0.35 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-Flash and Qwen3-VL 30B-A3BGLM-4.5-Flash 131,072 · Qwen3-VL 30B-A3B 131,072 · Mistral Nemotron 128,000 tokens
- Widest inputsQwen3-VL 30B-A3BGLM-4.5-Flash: Text · Mistral Nemotron: Text · Qwen3-VL 30B-A3B: Text, Images
- Self-hostingMistral Nemotron and Qwen3-VL 30B-A3BPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Mistral Nemotron | Qwen3-VL 30B-A3B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 72 |
| Inputs & features | 30% | 35 | 25 | 60 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 65/100 | 62/100 | 59/100 |
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) | Mistral NemotronNVIDIA | Qwen3-VL 30B-A3BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | Free (best) | $0.20 |
| Output | Free (best) | Free (best) | $0.80 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | Free (best) | $0.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Nvidia API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 98,304 tokens (best) | 8,192 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | glm-4.5-flash | mistralai/mistral-nemotron | qwen3-vl-30b-a3b |
| API providers | 4 (best) | 1 | 1 |
| Released | Jul 28, 2025 | Jun 11, 2025 | Apr 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.
- GLM-4.5-FlashFree
- Mistral NemotronFree
- Qwen3-VL 30B-A3B$3.60
Which should you choose?
Which is better: GLM-4.5-Flash, Mistral Nemotron or Qwen3-VL 30B-A3B?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Qwen3-VL 30B-A3B (59). Qwen3-VL 30B-A3B wins on inputs & features. 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, Mistral Nemotron or Qwen3-VL 30B-A3B?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Mistral Nemotron costs Free input / Free output per million tokens (official Nvidia API price); Qwen3-VL 30B-A3B costs $0.20 input / $0.80 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, Mistral Nemotron has not been scored yet and Qwen3-VL 30B-A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Mistral Nemotron and Qwen3-VL 30B-A3B 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?
GLM-4.5-Flash and Qwen3-VL 30B-A3B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemotron. Maximum output per response: GLM-4.5-Flash up to 98,304, Mistral Nemotron up to 8,192, Qwen3-VL 30B-A3B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Mistral Nemotron accepts text; Qwen3-VL 30B-A3B accepts text and images. Qwen3-VL 30B-A3B handles the widest range of inputs.
Are any of these open source?
Mistral Nemotron and Qwen3-VL 30B-A3B publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Mistral Nemotron came out Jun 11, 2025; Qwen3-VL 30B-A3B came out Apr 2025. Knowledge cutoff: GLM-4.5-Flash Apr 2025, Qwen3-VL 30B-A3B 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.
