Gemma 3 12B IT vs Llama-3.2-1B vs Qwen2.5-Coder-0.5B
Gemma 3 12B IT comes out ahead, 57 to 36 and 24 on our weighted score, and it is the cheaper option too.
- Our pick
GoogleGemma 3 12B IT
57/100- ECI123.5
- Price$0.05 / $0.15
- Context131K
MetaLlama-3.2-1B
36/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
24/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Gemma 3 12B IT is our pick
Gemma 3 12B IT is the better all-round choice, scoring 57/100 against Llama-3.2-1B (36) and Qwen2.5-Coder-0.5B (24). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemma 3 12B ITCapabilities Index (ECI): Gemma 3 12B IT 123.5 · Llama-3.2-1B 102.0 · Qwen2.5-Coder-0.5B 88.2
- Lowest priceGemma 3 12B ITGemma 3 12B IT $0.075 · Llama-3.2-1B $0.085 · Qwen2.5-Coder-0.5B $0.10 per 1M tokens (3:1 blend)
- Longest contextGemma 3 12B IT and Llama-3.2-1BGemma 3 12B IT 131,072 · Llama-3.2-1B 131,072 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsGemma 3 12B ITGemma 3 12B IT: Text, Images · Llama-3.2-1B: Text · Qwen2.5-Coder-0.5B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Gemma 3 12B IT | Llama-3.2-1B | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 45 | 17 | 0 |
| Price | 25% | 100 | 100 | 97 |
| Inputs & features | 15% | 50 | 0 | 0 |
| Context window | 10% | 24 | 24 | 0 |
| Overall | 100% | 57/100 | 36/100 | 24/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemma 3 12B ITGoogle | Llama-3.2-1BMeta | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 123.5 (best) | 102.0 | 88.2 |
| ECI rank | #138 of 148 (best) | #147 of 148 | #148 of 148 |
| GPQA DiamondGraduate-level science questions | 39.5% (best) | 23.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 16.7% (best) | 0.6% | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.064 | $0.10 |
| Output | $0.15 | $0.15 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.075 (best) | $0.085 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 32,768 tokens |
| Max output | 131,072 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 7 (best) | 2 | 1 |
| Released | Mar 12, 2025 | Sep 25, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Aug 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.
- Gemma 3 12B IT$0.80
- Llama-3.2-1B$0.936
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Gemma 3 12B IT, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Gemma 3 12B IT is the better all-round choice, scoring 57/100 against Llama-3.2-1B (36) and Qwen2.5-Coder-0.5B (24). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 3 12B IT, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Gemma 3 12B IT is cheaper at $0.05 input / $0.15 output per million tokens (median across 7 API providers). Llama-3.2-1B costs $0.064 input / $0.15 output per million tokens (median across 2 API providers); Qwen2.5-Coder-0.5B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for Gemma 3 12B IT versus $0.085 for Llama-3.2-1B (1.1× as much) and $0.10 for Qwen2.5-Coder-0.5B (1.3× as much).
Which scores higher on benchmarks?
Gemma 3 12B IT scores higher on the Capabilities Index (ECI): Gemma 3 12B IT 123.5 (#138 of 148), Llama-3.2-1B 102.0 (#147 of 148) and Qwen2.5-Coder-0.5B 88.2 (#148 of 148). Their confidence ranges do not overlap (115.6–129.3 vs 90.7–110.3), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 3 12B IT, Llama-3.2-1B and Qwen2.5-Coder-0.5B yet, so there is no like-for-like coding score. On overall capability, Gemma 3 12B IT leads, which tends to carry over to coding, but test on your own codebase. Note that Llama-3.2-1B and Qwen2.5-Coder-0.5B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemma 3 12B IT and Llama-3.2-1B have the largest context windows (131,072 and 131,072 tokens), against 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Gemma 3 12B IT up to 131,072, Llama-3.2-1B up to 8,192, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemma 3 12B IT accepts text and images; Llama-3.2-1B accepts text; Qwen2.5-Coder-0.5B accepts text. Gemma 3 12B 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 12B IT is the newest, released Mar 12, 2025. Qwen2.5-Coder-0.5B came out Nov 12, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Gemma 3 12B IT Aug 2024, Llama-3.2-1B 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 Oct 4, 2026.
