Llama-3.1-8B-Instruct vs Ministral 14B vs Ministral 3B
Too close to call on our weighted score (Ministral 14B 64, Ministral 3B 61, Llama-3.1-8B-Instruct 56). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Mistral AIMinistral 14B
64/100- ECI—
- Price$0.20 / $0.20
- Context262K
- Mistral AI
Ministral 3B
61/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Ministral 14B 64/100, Ministral 3B 61/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Ministral 3B on price and Ministral 14B for long inputs. 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 priceMinistral 3BMinistral 3B $0.10 · Llama-3.1-8B-Instruct $0.156 · Ministral 14B $0.20 per 1M tokens (3:1 blend)
- Longest contextMinistral 14BMinistral 14B 262,144 · Llama-3.1-8B-Instruct 128,000 · Ministral 3B 128,000 tokens
- Widest inputsMinistral 14BLlama-3.1-8B-Instruct: Text · Ministral 14B: Text, Images · Ministral 3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.1-8B-Instruct | Ministral 14B | Ministral 3B |
|---|---|---|---|---|
| Price | 50% | 88 | 83 | 97 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 37 | 24 |
| Overall | 100% | 56/100 | 64/100 | 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 | Llama-3.1-8B-InstructMeta | Ministral 14BMistral AI | 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 | $0.152 | $0.20 | $0.10 (best) |
| Output | $0.167 | $0.20 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.156 | $0.20 | $0.10 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 9 providers | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 4,096 tokens | 262,144 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | 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 | OpenApache-2.0 | Open |
| API model ID | — | — | — |
| API providers | 9 (best) | 1 | 1 |
| Released | Jul 23, 2024 | Dec 2, 2025 | Oct 16, 2024 |
| Knowledge cutoff | 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.
- Llama-3.1-8B-Instruct$1.85
- Ministral 14B$2.40
- Ministral 3B$1.20
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Ministral 14B or Ministral 3B?
It is close. Our weighted score puts them within 3 points (Ministral 14B 64/100, Ministral 3B 61/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Ministral 3B on price and Ministral 14B for long inputs. 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, Ministral 14B or Ministral 3B?
Ministral 3B is cheaper at $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); Ministral 14B costs $0.20 input / $0.20 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.10 per million tokens for Ministral 3B versus $0.156 for Llama-3.1-8B-Instruct (1.6× as much) and $0.20 for Ministral 14B (2× 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, Ministral 14B has not been scored yet and Ministral 3B has an ECI of 118.1.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Ministral 14B 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?
Ministral 14B has the largest context window at 262,144 tokens, against 128,000 for Llama-3.1-8B-Instruct and 128,000 for Ministral 3B. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Ministral 14B up to 262,144, Ministral 3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Ministral 14B accepts text and images; Ministral 3B accepts text. Ministral 14B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Ministral 14B is the newest, released Dec 2, 2025. Ministral 3B came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: 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.
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