Qwen2.5 7B Instruct vs Saba vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 62, Llama-3.1-8B-Instruct 62, Saba 46). The right pick depends on what you value most.
Alibaba (Qwen)Qwen2.5 7B Instruct
62/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
MetaLlama-3.1-8B-Instruct
62/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Saba 46/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B Instruct 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 priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 · Saba $0.30 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Saba 32,768 tokens
- Widest inputsSabaQwen2.5 7B Instruct: Text · Saba: Text, PDFs · Llama-3.1-8B-Instruct: Text
- Self-hostingQwen2.5 7B Instruct and Llama-3.1-8B-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Qwen2.5 7B Instruct | Saba | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 72 | 100 |
| Inputs & features | 30% | 25 | 35 | 25 |
| Context window | 20% | 25 | 0 | 24 |
| Overall | 100% | 62/100 | 46/100 | 62/100 |
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 | Qwen2.5 7B InstructAlibaba (Qwen) | SabaMistral AI | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.5 (best) | — | 116.4 |
| ECI rank | #159 of 165 (best) | — | #162 of 165 |
| GPQA DiamondGraduate-level science questions | 35.5% (best) | — | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 2.5% (best) | — | 1.7% |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.20 | $0.06 |
| Output | $0.10 | $0.60 | $0.08 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.063 (best) | $0.30 | $0.065 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 4 providers | Median of 17 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 26,214 tokens (best) | 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 | 4 | 4 | 18 (best) |
| Released | Sep 19, 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.
- Qwen2.5 7B Instruct$0.70
- Saba$3.20
- Llama-3.1-8B-Instruct$0.76
Which should you choose?
Which is better: Qwen2.5 7B Instruct, Saba or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Saba 46/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price and Qwen2.5 7B Instruct 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, Qwen2.5 7B Instruct, Saba or Llama-3.1-8B-Instruct?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Llama-3.1-8B-Instruct costs $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 $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.065 for Llama-3.1-8B-Instruct (1× as much) and $0.30 for Saba (4.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5 7B Instruct has an ECI of 118.5, 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 Qwen2.5 7B Instruct, 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?
Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 32,768 for Saba. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Saba up to 26,214, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct 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?
Qwen2.5 7B Instruct 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. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 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.
