Mistral 7B Instruct v0.3 vs Gemma 2 27B vs Llama-3.1-8B-Instruct
Llama-3.1-8B-Instruct comes out ahead, 32 to 28 and 22 on our weighted score, and it is the cheaper option too.
Mistral AIMistral 7B Instruct v0.3
22/100- ECI109.0
- Price—
- Context33K
GoogleGemma 2 27B
28/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
- Our pick
MetaLlama-3.1-8B-Instruct
32/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Llama-3.1-8B-Instruct is our pick
Llama-3.1-8B-Instruct is the better all-round choice, scoring 32/100 against Gemma 2 27B (28) and Mistral 7B Instruct v0.3 (22). It leads on context window. Gemma 2 27B wins on capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityGemma 2 27BCapabilities Index (ECI): Gemma 2 27B 122.1 · Llama-3.1-8B-Instruct 116.4 · Mistral 7B Instruct v0.3 109.0
- Lowest priceLlama-3.1-8B-InstructLlama-3.1-8B-Instruct $0.065 · Gemma 2 27B $0.65 per 1M tokens (3:1 blend) · Mistral 7B Instruct v0.3 unpriced
- Longest contextLlama-3.1-8B-InstructLlama-3.1-8B-Instruct 128,000 · Mistral 7B Instruct v0.3 32,768 · Gemma 2 27B 8,192 tokens
- Widest inputsSame inputsMistral 7B Instruct v0.3: Text · Gemma 2 27B: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral 7B Instruct v0.3 | Gemma 2 27B | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 26 | 42 | 35 |
| Inputs & features | 20% | 25 | 0 | 25 |
| Context window | 13% | 0 | 0 | 24 |
| Overall | 100% | 22/100 | 28/100 | 32/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral 7B Instruct v0.3Mistral AI | Gemma 2 27BGoogle | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 109.0 | 122.1 (best) | 116.4 |
| ECI rank | #164 of 165 | #152 of 165 (best) | #162 of 165 |
| GPQA DiamondGraduate-level science questions | 15.2% | 36.5% (best) | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.3% | 1.4% | 1.7% (best) |
| Price per million tokens | |||
| Input | — | $0.65 | $0.06 (best) |
| Output | — | $0.65 | $0.08 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.65 | $0.065 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 3 providers | Median of 17 providers |
| Limits | |||
| Context window | 32,768 tokens | 8,192 tokens | 128,000 tokens (best) |
| Max output | 32,768 tokens (best) | 2,048 tokens | 4,096 tokens |
| 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 | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Open |
| API model ID | — | — | — |
| API providers | 1 | 3 | 18 (best) |
| Released | May 22, 2024 | Jul 13, 2024 | Jul 23, 2024 |
| Knowledge cutoff | — | Jun 30, 2024 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral 7B Instruct v0.3—
- Gemma 2 27B$7.80
- Llama-3.1-8B-Instruct$0.76
Which should you choose?
Which is better: Mistral 7B Instruct v0.3, Gemma 2 27B or Llama-3.1-8B-Instruct?
Llama-3.1-8B-Instruct is the better all-round choice, scoring 32/100 against Gemma 2 27B (28) and Mistral 7B Instruct v0.3 (22). It leads on context window. Gemma 2 27B wins on capability. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Mistral 7B Instruct v0.3, Gemma 2 27B or Llama-3.1-8B-Instruct?
Llama-3.1-8B-Instruct is cheaper at $0.06 input / $0.08 output per million tokens (median across 17 API providers). Gemma 2 27B costs $0.65 input / $0.65 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.065 per million tokens for Llama-3.1-8B-Instruct versus $0.65 for Gemma 2 27B (10× as much). Mistral 7B Instruct v0.3 has no published per-token price.
Which scores higher on benchmarks?
Gemma 2 27B scores higher on the Capabilities Index (ECI): Gemma 2 27B 122.1 (#152 of 165), Llama-3.1-8B-Instruct 116.4 (#162 of 165) and Mistral 7B Instruct v0.3 109.0 (#164 of 165). The confidence ranges of the top two overlap (115.8–124.1 vs 107.0–121.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemma 2 27B 36.5%, Llama-3.1-8B-Instruct 27.0%, Mistral 7B Instruct v0.3 15.2%; OTIS Mock AIME 2024–2025 — Llama-3.1-8B-Instruct 1.7%, Gemma 2 27B 1.4%, Mistral 7B Instruct v0.3 0.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral 7B Instruct v0.3, Gemma 2 27B and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. On overall capability, Gemma 2 27B leads, which tends to carry over to coding, but test on your own codebase. Note that Gemma 2 27B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.1-8B-Instruct has the largest context window at 128,000 tokens, against 32,768 for Mistral 7B Instruct v0.3 and 8,192 for Gemma 2 27B. Maximum output per response: Mistral 7B Instruct v0.3 up to 32,768, Gemma 2 27B up to 2,048, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Mistral 7B Instruct v0.3 accepts text; Gemma 2 27B accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Llama-3.1-8B-Instruct is the newest, released Jul 23, 2024. Gemma 2 27B came out Jul 13, 2024; Mistral 7B Instruct v0.3 came out May 22, 2024. Knowledge cutoff: Gemma 2 27B Jun 30, 2024, Llama-3.1-8B-Instruct Dec 2023.
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 Nov 2, 2026.
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