Qwen2.5 7B Instruct vs Qwen2.5-Coder-32B-Instruct 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, Qwen2.5-Coder-32B-Instruct 39). 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
- Alibaba (Qwen)
Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
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, Qwen2.5-Coder-32B-Instruct 39/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price. 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 · Qwen2.5-Coder-32B-Instruct $0.745 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B Instruct and Qwen2.5-Coder-32B-InstructQwen2.5 7B Instruct 131,072 · Qwen2.5-Coder-32B-Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsQwen2.5 7B Instruct: Text · Qwen2.5-Coder-32B-Instruct: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5 7B Instruct | Qwen2.5-Coder-32B-Instruct | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 54 | 100 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 25 | 25 | 24 |
| Overall | 100% | 62/100 | 39/100 | 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) | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | 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) | $0.66 | $0.06 |
| Output | $0.10 | $1.00 | $0.08 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.063 (best) | $0.745 | $0.065 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 7 providers | Median of 17 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens (best) | 8,192 tokens (best) | 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Open |
| API model ID | — | — | — |
| API providers | 4 | 7 | 18 (best) |
| Released | Sep 19, 2024 | Nov 12, 2024 | Jul 23, 2024 |
| Knowledge cutoff | — | — | 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
- Qwen2.5-Coder-32B-Instruct$8.60
- Llama-3.1-8B-Instruct$0.76
Which should you choose?
Which is better: Qwen2.5 7B Instruct, Qwen2.5-Coder-32B-Instruct 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, Qwen2.5-Coder-32B-Instruct 39/100), so choose by what matters most for your work: Qwen2.5 7B Instruct on price. 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, Qwen2.5-Coder-32B-Instruct 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); Qwen2.5-Coder-32B-Instruct costs $0.66 input / $1.00 output per million tokens (median across 7 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 $0.745 for Qwen2.5-Coder-32B-Instruct (12× 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, Qwen2.5-Coder-32B-Instruct 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, Qwen2.5-Coder-32B-Instruct 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 and Qwen2.5-Coder-32B-Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Qwen2.5-Coder-32B-Instruct up to 8,192, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct accepts text; Qwen2.5-Coder-32B-Instruct 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?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. 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.
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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