Llama 3.3 Nemotron Super 49B v1 vs GLM-4.5-Flash vs Voxtral Small 24B 2507
GLM-4.5-Flash comes out ahead, 65 to 60 and 55 on our weighted score, and it is the cheaper option too.
NVIDIALlama 3.3 Nemotron Super 49B v1
60/100- ECI—
- Price$0.15 / $0.15
- Context131K
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (60) and Voxtral Small 24B 2507 (55). It leads 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-FlashGLM-4.5-Flash Free · Llama 3.3 Nemotron Super 49B v1 $0.15 · Voxtral Small 24B 2507 $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1 and GLM-4.5-FlashLlama 3.3 Nemotron Super 49B v1 131,072 · GLM-4.5-Flash 131,072 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507Llama 3.3 Nemotron Super 49B v1: Text · GLM-4.5-Flash: Text · Voxtral Small 24B 2507: Text, Audio
- Self-hostingLlama 3.3 Nemotron Super 49B v1 and Voxtral Small 24B 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1 | GLM-4.5-Flash | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 89 | 100 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 60/100 | 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 | Llama 3.3 Nemotron Super 49B v1NVIDIA | GLM-4.5-FlashZ.ai (Zhipu) | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | Free (best) | $0.10 |
| Output | $0.15 | Free (best) | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | Free (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Z.AI API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 32,768 tokens |
| Max output | 131,072 tokens (best) | 98,304 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 | Open | Proprietary | OpenApache 2.0 |
| API model ID | nvidia/llama-3.3-nemotron-super-49b-v1 | glm-4.5-flash | voxtral-small-latest |
| API providers | 2 | 4 | 7 (best) |
| Released | Apr 7, 2025 | Jul 28, 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.
- Llama 3.3 Nemotron Super 49B v1$1.80
- GLM-4.5-FlashFree
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1, GLM-4.5-Flash or Voxtral Small 24B 2507?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Llama 3.3 Nemotron Super 49B v1 (60) and Voxtral Small 24B 2507 (55). It leads 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, Llama 3.3 Nemotron Super 49B v1, GLM-4.5-Flash 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 3.3 Nemotron Super 49B v1 costs $0.15 input / $0.15 output per million tokens (median across 1 API provider; free on Nvidia); 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. Llama 3.3 Nemotron Super 49B v1 has not been scored yet, GLM-4.5-Flash 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 Llama 3.3 Nemotron Super 49B v1, GLM-4.5-Flash 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?
Llama 3.3 Nemotron Super 49B v1 and GLM-4.5-Flash have the largest context windows (131,072 and 131,072 tokens), against 32,768 for Voxtral Small 24B 2507. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, GLM-4.5-Flash up to 98,304, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Llama 3.3 Nemotron Super 49B v1 accepts text; GLM-4.5-Flash 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 3.3 Nemotron Super 49B v1 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 3.3 Nemotron Super 49B v1 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.
