Llama-3.1-8B-Instruct vs Qwen2.5 7B Instruct vs Solar Pro 2
Too close to call on our weighted score (Qwen2.5 7B Instruct 62, Llama-3.1-8B-Instruct 62, Solar Pro 2 51). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
62/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
Alibaba (Qwen)Qwen2.5 7B Instruct
62/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
UpstageSolar Pro 2
51/100- ECI—
- Price$0.25 / $0.25
- Context66K
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, Solar Pro 2 51/100), 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 · Solar Pro 2 $0.25 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Solar Pro 2 65,536 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Qwen2.5 7B Instruct: Text · Solar Pro 2: Text
- Self-hostingLlama-3.1-8B-Instruct and Qwen2.5 7B InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Llama-3.1-8B-Instruct | Qwen2.5 7B Instruct | Solar Pro 2 |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 75 |
| Inputs & features | 30% | 25 | 25 | 35 |
| Context window | 20% | 24 | 25 | 12 |
| Overall | 100% | 62/100 | 62/100 | 51/100 |
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 | Qwen2.5 7B InstructAlibaba (Qwen) | Solar Pro 2Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.4 | 118.5 (best) | — |
| ECI rank | #162 of 165 | #159 of 165 (best) | — |
| GPQA DiamondGraduate-level science questions | 27.0% | 35.5% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | 2.5% (best) | — |
| Price per million tokens | |||
| Input | $0.06 | $0.05 (best) | $0.25 |
| Output | $0.08 (best) | $0.10 | $0.25 |
| Cached input | — | — | — |
| Blended (3:1) | $0.065 | $0.063 (best) | $0.25 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 17 providers | Median of 3 providers | Official Upstage API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 65,536 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 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 | Yesminimal · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Proprietary |
| API model ID | — | — | solar-pro2 |
| API providers | 18 (best) | 4 | 2 |
| Released | Jul 23, 2024 | Sep 19, 2024 | May 20, 2025 |
| Knowledge cutoff | Dec 2023 | — | Mar 2025 |
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$0.76
- Qwen2.5 7B Instruct$0.70
- Solar Pro 2$3.00
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Qwen2.5 7B Instruct or Solar Pro 2?
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 62/100, Llama-3.1-8B-Instruct 62/100, Solar Pro 2 51/100), 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, Llama-3.1-8B-Instruct, Qwen2.5 7B Instruct or Solar Pro 2?
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); Solar Pro 2 costs $0.25 input / $0.25 output per million tokens (official Upstage API price). 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 $0.25 for Solar Pro 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 116.4, Qwen2.5 7B Instruct has an ECI of 118.5 and Solar Pro 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Qwen2.5 7B Instruct and Solar Pro 2 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 65,536 for Solar Pro 2. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Qwen2.5 7B Instruct up to 8,192, Solar Pro 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Qwen2.5 7B Instruct accepts text; Solar Pro 2 accepts text. They handle the same number of input types.
Are any of these open source?
Llama-3.1-8B-Instruct and Qwen2.5 7B Instruct publishes its weights (Apache 2.0) and can be self-hosted; Solar Pro 2 is proprietary.
Which is newer?
Solar Pro 2 is the newest, released May 20, 2025. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct Dec 2023, Solar Pro 2 Mar 2025.
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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