Qwen Flash vs Llama 3.1 Nemotron 70B Instruct vs Voxtral Small 24B 2507
Qwen Flash comes out ahead, 68 to 55 and 45 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (Qwen)Qwen Flash
68/100- ECI—
- Price$0.05 / $0.40
- Context1M
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Qwen Flash is our pick
Qwen Flash is the better all-round choice, scoring 68/100 against Voxtral Small 24B 2507 (55) and Llama 3.1 Nemotron 70B Instruct (45). It leads on price and context window. 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 priceQwen FlashQwen Flash $0.138 · Voxtral Small 24B 2507 $0.15 · Llama 3.1 Nemotron 70B Instruct $0.485 per 1M tokens (3:1 blend)
- Longest contextQwen FlashQwen Flash 1,000,000 · Llama 3.1 Nemotron 70B Instruct 128,000 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507Qwen Flash: Text · Llama 3.1 Nemotron 70B Instruct: Text · Voxtral Small 24B 2507: Text, Audio
- Self-hostingLlama 3.1 Nemotron 70B Instruct and Voxtral Small 24B 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen Flash | Llama 3.1 Nemotron 70B Instruct | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 91 | 65 | 89 |
| Inputs & features | 30% | 35 | 25 | 35 |
| Context window | 20% | 60 | 24 | 0 |
| Overall | 100% | 68/100 | 45/100 | 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 | Qwen FlashAlibaba (Qwen) | Llama 3.1 Nemotron 70B InstructNVIDIA | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.478 | $0.10 |
| Output | $0.40 | $0.504 | $0.30 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.138 (best) | $0.485 | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Official Mistral API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 128,000 tokens | 32,768 tokens |
| Max output | 32,768 tokens (best) | 8,192 tokens | 32,768 tokens (best) |
| 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 | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenApache 2.0 |
| API model ID | qwen-flash | nvidia/llama-3.1-nemotron-70b-instruct | voxtral-small-latest |
| API providers | 6 | 3 | 7 (best) |
| Released | Jul 28, 2025 | Apr 15, 2025 | Jul 15, 2025 |
| Knowledge cutoff | Apr 2024 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen Flash$1.30
- Llama 3.1 Nemotron 70B Instruct$5.79
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Qwen Flash, Llama 3.1 Nemotron 70B Instruct or Voxtral Small 24B 2507?
Qwen Flash is the better all-round choice, scoring 68/100 against Voxtral Small 24B 2507 (55) and Llama 3.1 Nemotron 70B Instruct (45). It leads on price and context window. 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, Qwen Flash, Llama 3.1 Nemotron 70B Instruct or Voxtral Small 24B 2507?
Qwen Flash is cheaper at $0.05 input / $0.40 output per million tokens (official Alibaba API price). Voxtral Small 24B 2507 costs $0.10 input / $0.30 output per million tokens (official Mistral API price); Llama 3.1 Nemotron 70B Instruct costs $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for Qwen Flash versus $0.15 for Voxtral Small 24B 2507 (1.1× as much) and $0.485 for Llama 3.1 Nemotron 70B Instruct (3.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen Flash has not been scored yet, Llama 3.1 Nemotron 70B Instruct 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 Qwen Flash, Llama 3.1 Nemotron 70B Instruct 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?
Qwen Flash has the largest context window at 1,000,000 tokens, against 128,000 for Llama 3.1 Nemotron 70B Instruct and 32,768 for Voxtral Small 24B 2507. Maximum output per response: Qwen Flash up to 32,768, Llama 3.1 Nemotron 70B Instruct up to 8,192, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen Flash accepts text; Llama 3.1 Nemotron 70B Instruct 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 70B Instruct and Voxtral Small 24B 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen Flash is proprietary.
Which is newer?
Qwen Flash is the newest, released Jul 28, 2025. Voxtral Small 24B 2507 came out Jul 15, 2025; Llama 3.1 Nemotron 70B Instruct came out Apr 15, 2025. Knowledge cutoff: Qwen Flash Apr 2024.
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.
