Llama 3.1 Nemotron 70B Instruct vs Pixtral Large (25.02) vs Qwen3-VL 30B-A3B
Qwen3-VL 30B-A3B comes out ahead, 59 to 45 and 33 on our weighted score, and it is the cheaper option too.
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen3-VL 30B-A3B
59/100- ECI—
- Price$0.20 / $0.80
- Context131K
Qwen3-VL 30B-A3B is our pick
Qwen3-VL 30B-A3B is the better all-round choice, scoring 59/100 against Llama 3.1 Nemotron 70B Instruct (45) and Pixtral Large (25.02) (33). It leads on price and inputs & features. 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 priceQwen3-VL 30B-A3BQwen3-VL 30B-A3B $0.35 · Llama 3.1 Nemotron 70B Instruct $0.485 · Pixtral Large (25.02) $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL 30B-A3BQwen3-VL 30B-A3B 131,072 · Llama 3.1 Nemotron 70B Instruct 128,000 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsPixtral Large (25.02) and Qwen3-VL 30B-A3BLlama 3.1 Nemotron 70B Instruct: Text · Pixtral Large (25.02): Text, Images · Qwen3-VL 30B-A3B: Text, Images
- Self-hostingLlama 3.1 Nemotron 70B Instruct and Qwen3-VL 30B-A3BPublishes downloadable weights
| Measure | Weight | Llama 3.1 Nemotron 70B Instruct | Pixtral Large (25.02) | Qwen3-VL 30B-A3B |
|---|---|---|---|---|
| Price | 50% | 65 | 27 | 72 |
| Inputs & features | 30% | 25 | 50 | 60 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 45/100 | 33/100 | 59/100 |
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 3.1 Nemotron 70B InstructNVIDIA | Pixtral Large (25.02)Mistral AI | Qwen3-VL 30B-A3BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.478 | $2.00 | $0.20 (best) |
| Output | $0.504 (best) | $6.00 | $0.80 |
| Cached input | — | — | — |
| Blended (3:1) | $0.485 | $3.00 | $0.35 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 3 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | nvidia/llama-3.1-nemotron-70b-instruct | — | qwen3-vl-30b-a3b |
| API providers | 3 (best) | 3 (best) | 1 |
| Released | Apr 15, 2025 | Apr 8, 2025 | Apr 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 3.1 Nemotron 70B Instruct$5.79
- Pixtral Large (25.02)$32.00
- Qwen3-VL 30B-A3B$3.60
Which should you choose?
Which is better: Llama 3.1 Nemotron 70B Instruct, Pixtral Large (25.02) or Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B is the better all-round choice, scoring 59/100 against Llama 3.1 Nemotron 70B Instruct (45) and Pixtral Large (25.02) (33). It leads on price and inputs & features. 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 3.1 Nemotron 70B Instruct, Pixtral Large (25.02) or Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B is cheaper at $0.20 input / $0.80 output per million tokens (official Alibaba 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); Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.35 per million tokens for Qwen3-VL 30B-A3B versus $0.485 for Llama 3.1 Nemotron 70B Instruct (1.4× as much) and $3.00 for Pixtral Large (25.02) (8.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama 3.1 Nemotron 70B Instruct has not been scored yet, Pixtral Large (25.02) has not been scored yet and Qwen3-VL 30B-A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 3.1 Nemotron 70B Instruct, Pixtral Large (25.02) and Qwen3-VL 30B-A3B 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?
Qwen3-VL 30B-A3B has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron 70B Instruct and 128,000 for Pixtral Large (25.02). Maximum output per response: Llama 3.1 Nemotron 70B Instruct up to 8,192, Pixtral Large (25.02) up to 8,192, Qwen3-VL 30B-A3B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Llama 3.1 Nemotron 70B Instruct accepts text; Pixtral Large (25.02) accepts text and images; Qwen3-VL 30B-A3B accepts text and images. Pixtral Large (25.02) handles the widest range of inputs.
Are any of these open source?
Llama 3.1 Nemotron 70B Instruct and Qwen3-VL 30B-A3B publishes its weights and can be self-hosted; Pixtral Large (25.02) is proprietary.
Which is newer?
Llama 3.1 Nemotron 70B Instruct is the newest, released Apr 15, 2025. Pixtral Large (25.02) came out Apr 8, 2025; Qwen3-VL 30B-A3B came out Apr 2025. Knowledge cutoff: Qwen3-VL 30B-A3B 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.
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