Qwen2.5 7B Instruct vs Gemma 3n E2B IT vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Qwen2.5 7B Instruct 25, Llama-3.1-8B-Instruct 25, Gemma 3n E2B IT 21). The right pick depends on what you value most.
Alibaba (Qwen)Qwen2.5 7B Instruct
25/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
GoogleGemma 3n E2B IT
21/100- ECI—
- Price—
- Context33K
MetaLlama-3.1-8B-Instruct
25/100- 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 25/100, Llama-3.1-8B-Instruct 25/100, Gemma 3n E2B IT 21/100), 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 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 priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.1-8B-Instruct $0.065 per 1M tokens (3:1 blend) · Gemma 3n E2B IT unpriced
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Gemma 3n E2B IT 32,768 tokens
- Widest inputsGemma 3n E2B ITQwen2.5 7B Instruct: Text · Gemma 3n E2B IT: Text, Images, Audio · 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 | Gemma 3n E2B IT | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 35 | 25 |
| Context window | 40% | 25 | 0 | 24 |
| Overall | 100% | 25/100 | 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 | Qwen2.5 7B InstructAlibaba (Qwen) | Gemma 3n E2B ITGoogle | 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) | — | $0.06 |
| Output | $0.10 | — | $0.08 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.063 (best) | — | $0.065 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 3 providers | — | Median of 17 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Max output | 8,192 tokens | 32,768 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | OpenGemma Terms of Use | Open |
| API model ID | — | — | — |
| API providers | 4 | — | 18 (best) |
| Released | Sep 19, 2024 | Jun 26, 2025 | Jul 23, 2024 |
| Knowledge cutoff | — | Jun 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
- Gemma 3n E2B IT—
- Llama-3.1-8B-Instruct$0.76
Which should you choose?
Which is better: Qwen2.5 7B Instruct, Gemma 3n E2B IT or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within a point (Qwen2.5 7B Instruct 25/100, Llama-3.1-8B-Instruct 25/100, Gemma 3n E2B IT 21/100), 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 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, Qwen2.5 7B Instruct, Gemma 3n E2B IT 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). 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). Gemma 3n E2B IT has no published per-token price.
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, Gemma 3n E2B IT 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, Gemma 3n E2B IT 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. Note that Gemma 3n E2B IT does not support tool calling, which most coding agents need.
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 Gemma 3n E2B IT. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Gemma 3n E2B IT up to 32,768, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct accepts text; Gemma 3n E2B IT accepts text, images and audio; Llama-3.1-8B-Instruct accepts text. Gemma 3n E2B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0 and Gemma Terms of Use), so you can self-host them.
Which is newer?
Gemma 3n E2B IT is the newest, released Jun 26, 2025. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Gemma 3n E2B IT Jun 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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