Llama-3.2-1B vs Qwen2.5 7B Instruct vs Gemma 3 4B IT
Too close to call on our weighted score (Gemma 3 4B IT 43, Qwen2.5 7B Instruct 41, Llama-3.2-1B 34). The right pick depends on what you value most.
MetaLlama-3.2-1B
34/100- ECI—
- Price$0.027 / $0.201
- Context131K
Alibaba (Qwen)Qwen2.5 7B Instruct
41/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
GoogleGemma 3 4B IT
43/100- ECI115.8
- Price$0.05 / $0.10
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Gemma 3 4B IT 43/100, Qwen2.5 7B Instruct 41/100, Llama-3.2-1B 34/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityQwen2.5 7B InstructShared benchmarks: Qwen2.5 7B Instruct 19.0% · Gemma 3 4B IT 15.4% · Llama-3.2-1B 12.2%
- Lowest priceQwen2.5 7B Instruct and Gemma 3 4B ITQwen2.5 7B Instruct $0.063 · Gemma 3 4B IT $0.063 · Llama-3.2-1B $0.07 per 1M tokens (3:1 blend)
- Longest contextAbout the sameLlama-3.2-1B 131,072 · Qwen2.5 7B Instruct 131,072 · Gemma 3 4B IT 131,072 tokens
- Widest inputsGemma 3 4B ITLlama-3.2-1B: Text · Qwen2.5 7B Instruct: Text · Gemma 3 4B IT: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-1B | Qwen2.5 7B Instruct | Gemma 3 4B IT |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 12 | 19 | 15 |
| Price | 25% | 100 | 100 | 100 |
| Inputs & features | 15% | 0 | 25 | 50 |
| Context window | 10% | 25 | 25 | 25 |
| Overall | 100% | 34/100 | 41/100 | 43/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.2-1BMeta | Qwen2.5 7B InstructAlibaba (Qwen) | Gemma 3 4B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 118.5 (best) | 115.8 |
| ECI rank | — | #159 of 165 (best) | #163 of 165 |
| GPQA DiamondGraduate-level science questions | 23.9% | 35.5% (best) | 23.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | 2.5% | 7.5% (best) |
| Price per million tokens | |||
| Input | $0.027 (best) | $0.05 | $0.05 |
| Output | $0.201 | $0.10 (best) | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 | $0.063 (best) | $0.063 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 6 providers | Median of 3 providers | Median of 9 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | OpenApache 2.0 | Open |
| API model ID | — | — | — |
| API providers | 6 | 4 | 10 (best) |
| Released | Sep 25, 2024 | Sep 19, 2024 | Mar 12, 2025 |
| Knowledge cutoff | Dec 2023 | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-1B$0.672
- Qwen2.5 7B Instruct$0.70
- Gemma 3 4B IT$0.70
Which should you choose?
Which is better: Llama-3.2-1B, Qwen2.5 7B Instruct or Gemma 3 4B IT?
It is close. Our weighted score puts them within 2 points (Gemma 3 4B IT 43/100, Qwen2.5 7B Instruct 41/100, Llama-3.2-1B 34/100), so choose by what matters most for your work: Qwen2.5 7B Instruct for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Llama-3.2-1B, Qwen2.5 7B Instruct or Gemma 3 4B IT?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Gemma 3 4B IT costs $0.05 input / $0.10 output per million tokens (median across 9 API providers); Llama-3.2-1B costs $0.027 input / $0.201 output per million tokens (median across 6 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 $0.063 for Gemma 3 4B IT (1× as much) and $0.07 for Llama-3.2-1B (1.1× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Qwen2.5 7B Instruct 19.0%, Gemma 3 4B IT 15.4% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Llama-3.2-1B 23.9%, Gemma 3 4B IT 23.2%; OTIS Mock AIME 2024–2025 — Gemma 3 4B IT 7.5%, Qwen2.5 7B Instruct 2.5%, Llama-3.2-1B 0.6%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Qwen2.5 7B Instruct and Gemma 3 4B IT yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 7B Instruct leads, which tends to carry over to coding, but test on your own codebase. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B, Qwen2.5 7B Instruct and Gemma 3 4B IT share the same 131,072-token context window. Maximum output per response: Llama-3.2-1B up to 8,192, Qwen2.5 7B Instruct up to 8,192, Gemma 3 4B IT up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Qwen2.5 7B Instruct accepts text; Gemma 3 4B IT accepts text and images. Gemma 3 4B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Llama 3.2 Community License and Apache 2.0), so you can self-host them.
Which is newer?
Gemma 3 4B IT is the newest, released Mar 12, 2025. Llama-3.2-1B came out Sep 25, 2024; Qwen2.5 7B Instruct came out Sep 19, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Gemma 3 4B IT Aug 2024.
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.
