Command R vs Llama 3.1 Nemotron 70B Instruct vs Qwen3-VL 30B-A3B
Qwen3-VL 30B-A3B comes out ahead, 59 to 51 and 45 on our weighted score, though Command R is 25% cheaper per token.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- 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 Command R (51) and Llama 3.1 Nemotron 70B Instruct (45). It leads on inputs & features. Command R wins 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 priceCommand RCommand R $0.263 · Qwen3-VL 30B-A3B $0.35 · Llama 3.1 Nemotron 70B Instruct $0.485 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL 30B-A3BQwen3-VL 30B-A3B 131,072 · Command R 128,000 · Llama 3.1 Nemotron 70B Instruct 128,000 tokens
- Widest inputsQwen3-VL 30B-A3BCommand R: Text · Llama 3.1 Nemotron 70B Instruct: Text · Qwen3-VL 30B-A3B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R | Llama 3.1 Nemotron 70B Instruct | Qwen3-VL 30B-A3B |
|---|---|---|---|---|
| Price | 50% | 77 | 65 | 72 |
| Inputs & features | 30% | 25 | 25 | 60 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 45/100 | 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 | Command RCohere | Llama 3.1 Nemotron 70B InstructNVIDIA | Qwen3-VL 30B-A3BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.478 | $0.20 |
| Output | $0.60 | $0.504 (best) | $0.80 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.485 | $0.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | 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 | Open | Open |
| API model ID | command-r-08-2024 | nvidia/llama-3.1-nemotron-70b-instruct | qwen3-vl-30b-a3b |
| API providers | 5 (best) | 3 | 1 |
| Released | Aug 30, 2024 | Apr 15, 2025 | Apr 2025 |
| Knowledge cutoff | Jun 1, 2024 | — | 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.
- Command R$2.70
- Llama 3.1 Nemotron 70B Instruct$5.79
- Qwen3-VL 30B-A3B$3.60
Which should you choose?
Which is better: Command R, Llama 3.1 Nemotron 70B Instruct or Qwen3-VL 30B-A3B?
Qwen3-VL 30B-A3B is the better all-round choice, scoring 59/100 against Command R (51) and Llama 3.1 Nemotron 70B Instruct (45). It leads on inputs & features. Command R wins 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, Command R, Llama 3.1 Nemotron 70B Instruct or Qwen3-VL 30B-A3B?
Command R is cheaper at $0.15 input / $0.60 output per million tokens (official Cohere API price). Qwen3-VL 30B-A3B costs $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). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for Command R versus $0.35 for Qwen3-VL 30B-A3B (1.3× as much) and $0.485 for Llama 3.1 Nemotron 70B Instruct (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R has not been scored yet, Llama 3.1 Nemotron 70B Instruct 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 Command R, Llama 3.1 Nemotron 70B Instruct 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 Command R and 128,000 for Llama 3.1 Nemotron 70B Instruct. Maximum output per response: Command R up to 4,000, Llama 3.1 Nemotron 70B Instruct up to 8,192, Qwen3-VL 30B-A3B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Llama 3.1 Nemotron 70B Instruct accepts text; Qwen3-VL 30B-A3B accepts text and images. Qwen3-VL 30B-A3B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Llama 3.1 Nemotron 70B Instruct is the newest, released Apr 15, 2025. Qwen3-VL 30B-A3B came out Apr 2025; Command R came out Aug 30, 2024. Knowledge cutoff: Command R Jun 1, 2024, 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.
