GLM-4.5-Flash vs Granite-4.0-H-Small vs Llama 3.1 Nemotron Ultra 253B
Too close to call on our weighted score (GLM-4.5-Flash 65, Llama 3.1 Nemotron Ultra 253B 65, Granite-4.0-H-Small 63). 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
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.5-Flash 65/100, Llama 3.1 Nemotron Ultra 253B 65/100, Granite-4.0-H-Small 63/100), 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 Llama 3.1 Nemotron Ultra 253BGLM-4.5-Flash Free · Llama 3.1 Nemotron Ultra 253B 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 · Llama 3.1 Nemotron Ultra 253B 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Flash: Text · Granite-4.0-H-Small: Text · Llama 3.1 Nemotron Ultra 253B: Text
- Self-hostingGranite-4.0-H-Small and Llama 3.1 Nemotron Ultra 253BPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Granite-4.0-H-Small | Llama 3.1 Nemotron Ultra 253B |
|---|---|---|---|---|
| Price | 50% | 100 | 95 | 100 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 65/100 | 63/100 | 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 | GLM-4.5-FlashZ.ai (Zhipu) | Granite-4.0-H-SmallIBM | Llama 3.1 Nemotron Ultra 253BNVIDIA |
|---|---|---|---|
| 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 | Yes |
| 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 | nvidia/llama-3.1-nemotron-ultra-253b-v1 |
| API providers | 4 (best) | 1 | 1 |
| Released | Jul 28, 2025 | Oct 2, 2025 | Apr 7, 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
- Llama 3.1 Nemotron Ultra 253BFree
Which should you choose?
Which is better: GLM-4.5-Flash, Granite-4.0-H-Small or Llama 3.1 Nemotron Ultra 253B?
It is close. Our weighted score puts them within a point (GLM-4.5-Flash 65/100, Llama 3.1 Nemotron Ultra 253B 65/100, Granite-4.0-H-Small 63/100), 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 Llama 3.1 Nemotron Ultra 253B?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Llama 3.1 Nemotron Ultra 253B 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 Llama 3.1 Nemotron Ultra 253B 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 Llama 3.1 Nemotron Ultra 253B 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 Llama 3.1 Nemotron Ultra 253B. Maximum output per response: GLM-4.5-Flash up to 98,304, Granite-4.0-H-Small up to 131,072, Llama 3.1 Nemotron Ultra 253B 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; Llama 3.1 Nemotron Ultra 253B accepts text. They handle the same number of input types.
Are any of these open source?
Granite-4.0-H-Small and Llama 3.1 Nemotron Ultra 253B 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; Llama 3.1 Nemotron Ultra 253B came out Apr 7, 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.
