Mistral 7B Instruct v0.3 vs Saba vs Llama-3.1-8B-Instruct
Llama-3.1-8B-Instruct comes out ahead, 25 to 21 and 15 on our weighted score, and it is the cheaper option too.
Mistral AIMistral 7B Instruct v0.3
15/100- ECI109.0
- Price—
- Context33K
- Mistral AI
Saba
21/100- ECI—
- Price$0.20 / $0.60
- Context33K
- Our pick
MetaLlama-3.1-8B-Instruct
25/100- 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 25/100 against Saba (21) and Mistral 7B Instruct v0.3 (15). It leads on context window. Saba wins on inputs & features. The score weighs inputs & features 60%, context window 40%. 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 priceLlama-3.1-8B-InstructLlama-3.1-8B-Instruct $0.065 · Saba $0.30 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 · Saba 32,768 tokens
- Widest inputsSabaMistral 7B Instruct v0.3: Text · Saba: Text, PDFs · Llama-3.1-8B-Instruct: Text
- Self-hostingMistral 7B Instruct v0.3 and Llama-3.1-8B-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Mistral 7B Instruct v0.3 | Saba | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 35 | 25 |
| Context window | 40% | 0 | 0 | 24 |
| Overall | 100% | 15/100 | 21/100 | 25/100 |
Left out because at least one model lacks the data: capability and 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 | SabaMistral AI | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 109.0 | — | 116.4 (best) |
| ECI rank | #164 of 165 | — | #162 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 15.2% | — | 27.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.3% | — | 1.7% (best) |
| Price per million tokens | |||
| Input | — | $0.20 | $0.06 (best) |
| Output | — | $0.60 | $0.08 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.30 | $0.065 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 4 providers | Median of 17 providers |
| Limits | |||
| Context window | 32,768 tokens | 32,768 tokens | 128,000 tokens (best) |
| Max output | 32,768 tokens (best) | 26,214 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | Yes | 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 | OpenApache 2.0 | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 1 | 4 | 18 (best) |
| Released | May 22, 2024 | Feb 17, 2025 | Jul 23, 2024 |
| Knowledge cutoff | — | Sep 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—
- Saba$3.20
- Llama-3.1-8B-Instruct$0.76
Which should you choose?
Which is better: Mistral 7B Instruct v0.3, Saba or Llama-3.1-8B-Instruct?
Llama-3.1-8B-Instruct is the better all-round choice, scoring 25/100 against Saba (21) and Mistral 7B Instruct v0.3 (15). It leads on context window. Saba wins on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Mistral 7B Instruct v0.3, Saba 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). Saba costs $0.20 input / $0.60 output per million tokens (median across 4 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 $0.30 for Saba (4.6× as much). Mistral 7B Instruct v0.3 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral 7B Instruct v0.3 has an ECI of 109.0, Saba has not been scored yet and Llama-3.1-8B-Instruct has an ECI of 116.4.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral 7B Instruct v0.3, Saba and Llama-3.1-8B-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?
Llama-3.1-8B-Instruct has the largest context window at 128,000 tokens, against 32,768 for Mistral 7B Instruct v0.3 and 32,768 for Saba. Maximum output per response: Mistral 7B Instruct v0.3 up to 32,768, Saba up to 26,214, 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; Saba accepts text and PDFs; Llama-3.1-8B-Instruct accepts text. Saba handles the widest range of inputs.
Are any of these open source?
Mistral 7B Instruct v0.3 and Llama-3.1-8B-Instruct publishes its weights (Apache 2.0) and can be self-hosted; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 2025. Llama-3.1-8B-Instruct came out Jul 23, 2024; Mistral 7B Instruct v0.3 came out May 22, 2024. Knowledge cutoff: Saba Sep 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.
