GLM-4.5-Flash vs Llama 3.1 Nemotron Ultra 253B vs Voxtral Small 24B 2507
Too close to call on our weighted score (GLM-4.5-Flash 65, Llama 3.1 Nemotron Ultra 253B 65, Voxtral Small 24B 2507 55). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
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, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: GLM-4.5-Flash on price and GLM-4.5-Flash 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 priceGLM-4.5-Flash and Llama 3.1 Nemotron Ultra 253BGLM-4.5-Flash Free · Llama 3.1 Nemotron Ultra 253B Free · Voxtral Small 24B 2507 $0.15 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-FlashGLM-4.5-Flash 131,072 · Llama 3.1 Nemotron Ultra 253B 128,000 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507GLM-4.5-Flash: Text · Llama 3.1 Nemotron Ultra 253B: Text · Voxtral Small 24B 2507: Text, Audio
- Self-hostingLlama 3.1 Nemotron Ultra 253B and Voxtral Small 24B 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GLM-4.5-Flash | Llama 3.1 Nemotron Ultra 253B | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 65/100 | 65/100 | 55/100 |
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) | Llama 3.1 Nemotron Ultra 253BNVIDIA | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | Free (best) | $0.10 |
| Output | Free (best) | Free (best) | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | Free (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Nvidia API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 32,768 tokens |
| Max output | 98,304 tokens (best) | 8,192 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenApache 2.0 |
| API model ID | glm-4.5-flash | nvidia/llama-3.1-nemotron-ultra-253b-v1 | voxtral-small-latest |
| API providers | 4 | 1 | 7 (best) |
| Released | Jul 28, 2025 | Apr 7, 2025 | Jul 15, 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
- Llama 3.1 Nemotron Ultra 253BFree
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: GLM-4.5-Flash, Llama 3.1 Nemotron Ultra 253B or Voxtral Small 24B 2507?
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, Voxtral Small 24B 2507 55/100), so choose by what matters most for your work: GLM-4.5-Flash on price and GLM-4.5-Flash 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, GLM-4.5-Flash, Llama 3.1 Nemotron Ultra 253B or Voxtral Small 24B 2507?
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); Voxtral Small 24B 2507 costs $0.10 input / $0.30 output per million tokens (official Mistral 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, Llama 3.1 Nemotron Ultra 253B has not been scored yet and Voxtral Small 24B 2507 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Llama 3.1 Nemotron Ultra 253B and Voxtral Small 24B 2507 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 has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron Ultra 253B and 32,768 for Voxtral Small 24B 2507. Maximum output per response: GLM-4.5-Flash up to 98,304, Llama 3.1 Nemotron Ultra 253B up to 8,192, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Llama 3.1 Nemotron Ultra 253B accepts text; Voxtral Small 24B 2507 accepts text and audio. Voxtral Small 24B 2507 handles the widest range of inputs.
Are any of these open source?
Llama 3.1 Nemotron Ultra 253B and Voxtral Small 24B 2507 publishes its weights (Apache 2.0) 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. Voxtral Small 24B 2507 came out Jul 15, 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.
