GLM-4.5-Flash vs Granite-4.0-H-Small vs Mistral Nemotron
Too close to call on our weighted score (GLM-4.5-Flash 65, Granite-4.0-H-Small 63, Mistral Nemotron 62). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
IBMGranite-4.0-H-Small
63/100- ECI—
- Price$0.064 / $0.265
- Context131K
NVIDIAMistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (GLM-4.5-Flash 65/100, Granite-4.0-H-Small 63/100, Mistral Nemotron 62/100), so choose by what matters most for your work: GLM-4.5-Flash 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 priceGLM-4.5-Flash and Mistral NemotronGLM-4.5-Flash Free · Mistral Nemotron Free · Granite-4.0-H-Small $0.114 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-Flash and Granite-4.0-H-SmallGLM-4.5-Flash 131,072 · Granite-4.0-H-Small 131,072 · Mistral Nemotron 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Flash: Text · Granite-4.0-H-Small: Text · Mistral Nemotron: Text
- Self-hostingGranite-4.0-H-Small and Mistral NemotronPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Granite-4.0-H-Small | Mistral Nemotron |
|---|---|---|---|---|
| Price | 50% | 100 | 95 | 100 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 65/100 | 63/100 | 62/100 |
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) | Granite-4.0-H-SmallIBM | Mistral NemotronNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | $0.064 | Free (best) |
| Output | Free (best) | $0.265 | Free (best) |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $0.114 | Free (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official watsonx.ai API | Official Nvidia API |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 98,304 tokens | 131,072 tokens (best) | 8,192 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 | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | glm-4.5-flash | ibm/granite-4-h-small | mistralai/mistral-nemotron |
| API providers | 4 (best) | 1 | 1 |
| Released | Jul 28, 2025 | Oct 2, 2025 | Jun 11, 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.
- GLM-4.5-FlashFree
- Granite-4.0-H-Small$1.17
- Mistral NemotronFree
Which should you choose?
Which is better: GLM-4.5-Flash, Granite-4.0-H-Small or Mistral Nemotron?
It is close. Our weighted score puts them within 3 points (GLM-4.5-Flash 65/100, Granite-4.0-H-Small 63/100, Mistral Nemotron 62/100), so choose by what matters most for your work: GLM-4.5-Flash 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-4.5-Flash, Granite-4.0-H-Small or Mistral Nemotron?
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); Granite-4.0-H-Small costs $0.064 input / $0.265 output per million tokens (official watsonx.ai 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, Granite-4.0-H-Small has not been scored yet and Mistral Nemotron has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Granite-4.0-H-Small and Mistral Nemotron 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 Granite-4.0-H-Small 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, Granite-4.0-H-Small up to 131,072, Mistral Nemotron up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Granite-4.0-H-Small accepts text; Mistral Nemotron accepts text. They handle the same number of input types.
Are any of these open source?
Granite-4.0-H-Small and Mistral Nemotron publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
Granite-4.0-H-Small is the newest, released Oct 2, 2025. 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.
