Mistral Nemotron vs Laguna M.1 vs GLM-4.5-Flash
Too close to call on our weighted score (Laguna M.1 68, GLM-4.5-Flash 65, Mistral Nemotron 62). The right pick depends on what you value most.
NVIDIAMistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Laguna M.1 68/100, GLM-4.5-Flash 65/100, Mistral Nemotron 62/100), so choose by what matters most for your work: Mistral Nemotron on price and Laguna M.1 for long inputs. 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 priceSame priceMistral Nemotron Free · Laguna M.1 Free · GLM-4.5-Flash Free per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · GLM-4.5-Flash 131,072 · Mistral Nemotron 128,000 tokens
- Widest inputsSame inputsMistral Nemotron: Text · Laguna M.1: Text · GLM-4.5-Flash: Text
- Self-hostingMistral Nemotron and Laguna M.1Publishes downloadable weights
| Measure | Weight | Mistral Nemotron | Laguna M.1 | GLM-4.5-Flash |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 25 | 35 | 35 |
| Context window | 20% | 24 | 37 | 24 |
| Overall | 100% | 62/100 | 68/100 | 65/100 |
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 | Mistral NemotronNVIDIA | Laguna M.1Poolside | GLM-4.5-FlashZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free | Free | Free |
| Output | Free | Free | Free |
| Cached input | — | — | — |
| Blended (3:1) | Free | Free | Free |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Nvidia API | Official Poolside API | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 32,768 tokens | 98,304 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | mistralai/mistral-nemotron | poolside/laguna-m.1 | glm-4.5-flash |
| API providers | 1 | 2 | 4 (best) |
| Released | Jun 11, 2025 | Apr 28, 2026 | Jul 28, 2025 |
| Knowledge cutoff | — | — | 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.
- Mistral NemotronFree
- Laguna M.1Free
- GLM-4.5-FlashFree
Which should you choose?
Which is better: Mistral Nemotron, Laguna M.1 or GLM-4.5-Flash?
It is close. Our weighted score puts them within 2 points (Laguna M.1 68/100, GLM-4.5-Flash 65/100, Mistral Nemotron 62/100), so choose by what matters most for your work: Mistral Nemotron on price and Laguna M.1 for long inputs. 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, Mistral Nemotron, Laguna M.1 or GLM-4.5-Flash?
Mistral Nemotron, Laguna M.1 and GLM-4.5-Flash cost the same: Free input / Free output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral Nemotron has not been scored yet, Laguna M.1 has not been scored yet and GLM-4.5-Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Nemotron, Laguna M.1 and GLM-4.5-Flash 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?
Laguna M.1 has the largest context window at 262,144 tokens, against 131,072 for GLM-4.5-Flash and 128,000 for Mistral Nemotron. Maximum output per response: Mistral Nemotron up to 8,192, Laguna M.1 up to 32,768, GLM-4.5-Flash up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemotron accepts text; Laguna M.1 accepts text; GLM-4.5-Flash accepts text. They handle the same number of input types.
Are any of these open source?
Mistral Nemotron and Laguna M.1 publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
Laguna M.1 is the newest, released Apr 28, 2026. GLM-4.5-Flash came out Jul 28, 2025; Mistral Nemotron came out Jun 11, 2025. Knowledge cutoff: GLM-4.5-Flash 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.
