Command R7B vs Llama-3.1-8B-Instruct vs Ministral 3B
Too close to call on our weighted score (Command R7B 62, Ministral 3B 61, Llama-3.1-8B-Instruct 56). The right pick depends on what you value most.
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Mistral AIMinistral 3B
61/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
Too close to call
It is close. Our weighted score puts them within 1 points (Command R7B 62/100, Ministral 3B 61/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Command R7B 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 R7BCommand R7B $0.066 · Ministral 3B $0.10 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextAbout the sameCommand R7B 128,000 · Llama-3.1-8B-Instruct 128,000 · Ministral 3B 128,000 tokens
- Widest inputsSame inputsCommand R7B: Text · Llama-3.1-8B-Instruct: Text · Ministral 3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R7B | Llama-3.1-8B-Instruct | Ministral 3B |
|---|---|---|---|---|
| Price | 50% | 100 | 88 | 97 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 56/100 | 61/100 |
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 R7BCohere | Llama-3.1-8B-InstructMeta | Ministral 3BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.6 | 118.1 (best) |
| ECI rank | — | #145 of 148 | #144 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 27.0% (best) | 25.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% | — |
| Price per million tokens | |||
| Input | $0.037 (best) | $0.152 | $0.10 |
| Output | $0.15 | $0.167 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.066 (best) | $0.156 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 9 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r7b-12-2024 | — | — |
| API providers | 5 | 9 (best) | 1 |
| Released | Dec 2, 2024 | Jul 23, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Dec 2023 | Mar 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Command R7B$0.675
- Llama-3.1-8B-Instruct$1.85
- Ministral 3B$1.20
Which should you choose?
Which is better: Command R7B, Llama-3.1-8B-Instruct or Ministral 3B?
It is close. Our weighted score puts them within 1 points (Command R7B 62/100, Ministral 3B 61/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Command R7B 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 R7B, Llama-3.1-8B-Instruct or Ministral 3B?
Command R7B is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). Ministral 3B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider); Llama-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.066 per million tokens for Command R7B versus $0.10 for Ministral 3B (1.5× as much) and $0.156 for Llama-3.1-8B-Instruct (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R7B has not been scored yet, Llama-3.1-8B-Instruct has an ECI of 116.6 and Ministral 3B has an ECI of 118.1.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B, Llama-3.1-8B-Instruct and Ministral 3B 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?
Command R7B, Llama-3.1-8B-Instruct and Ministral 3B share the same 128,000-token context window. Maximum output per response: Command R7B up to 4,000, Llama-3.1-8B-Instruct up to 4,096, Ministral 3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Command R7B accepts text; Llama-3.1-8B-Instruct accepts text; Ministral 3B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Command R7B is the newest, released Dec 2, 2024. Ministral 3B came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Command R7B Jun 1, 2024, Llama-3.1-8B-Instruct Dec 2023, Ministral 3B Mar 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.
