Llama-3.1-8B-Instruct vs Command R7B vs Ministral 8B Instruct
Command R7B comes out ahead, 62 to 57 and 56 on our weighted score, and it is the cheaper option too.
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
- Our pick
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
Command R7B is our pick
Command R7B is the better all-round choice, scoring 62/100 against Ministral 8B Instruct (57) and Llama-3.1-8B-Instruct (56). 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 priceCommand R7BCommand R7B $0.066 · Ministral 8B Instruct $0.15 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextMinistral 8B InstructMinistral 8B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Command R7B 128,000 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Command R7B: Text · Ministral 8B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.1-8B-Instruct | Command R7B | Ministral 8B Instruct |
|---|---|---|---|---|
| Price | 50% | 88 | 100 | 89 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 56/100 | 62/100 | 57/100 |
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-3.1-8B-InstructMeta | Command R7BCohere | Ministral 8B InstructMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.6 | — | — |
| ECI rank | #145 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 27.0% | — | 27.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | — | — |
| Price per million tokens | |||
| Input | $0.152 | $0.037 (best) | $0.15 |
| Output | $0.167 | $0.15 (best) | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.156 | $0.066 (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 9 providers | Official Cohere API | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 4,000 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 | OpenMistral Research License |
| API model ID | — | command-r7b-12-2024 | — |
| API providers | 9 (best) | 5 | 1 |
| Released | Jul 23, 2024 | Dec 2, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Dec 2023 | Jun 1, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.1-8B-Instruct$1.85
- Command R7B$0.675
- Ministral 8B Instruct$1.80
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Command R7B or Ministral 8B Instruct?
Command R7B is the better all-round choice, scoring 62/100 against Ministral 8B Instruct (57) and Llama-3.1-8B-Instruct (56). 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-3.1-8B-Instruct, Command R7B or Ministral 8B Instruct?
Command R7B is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). Ministral 8B Instruct costs $0.15 input / $0.15 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 $0.15 for Ministral 8B Instruct (2.3× 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. Llama-3.1-8B-Instruct has an ECI of 116.6, Command R7B has not been scored yet and Ministral 8B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Command R7B and Ministral 8B Instruct 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?
Ministral 8B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 128,000 for Command R7B. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Command R7B up to 4,000, Ministral 8B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Command R7B accepts text; Ministral 8B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Mistral Research License), so you can self-host them.
Which is newer?
Command R7B is the newest, released Dec 2, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct Dec 2023, Command R7B Jun 1, 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.
