Llama-3.2-11B-Vision-Instruct vs Llama-3.2-1B vs Qwen2.5-Coder-0.5B
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 55 and 49 on our weighted score, though Llama-3.2-1B is 3.2× cheaper per token.
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
- Meta
Llama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Llama-3.2-11B-Vision-Instruct is our pick
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features. Llama-3.2-1B wins 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 priceLlama-3.2-1BLlama-3.2-1B $0.085 · Qwen2.5-Coder-0.5B $0.10 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct: 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 | Llama-3.2-11B-Vision-Instruct | Llama-3.2-1B | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 76 | 100 | 97 |
| Inputs & features | 30% | 50 | 0 | 0 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 58/100 | 55/100 | 49/100 |
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-3.2-11B-Vision-InstructMeta | Llama-3.2-1BMeta | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 102.0 (best) | 88.2 |
| ECI rank | — | #147 of 148 (best) | #148 of 148 |
| GPQA DiamondGraduate-level science questions | — | 23.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 0.6% | — |
| Price per million tokens | |||
| Input | $0.197 | $0.064 (best) | $0.10 |
| Output | $0.51 | $0.15 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.275 | $0.085 (best) | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 32,768 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 8,192 tokens (best) |
| 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 | 2 (best) | 2 (best) | 1 |
| Released | Sep 25, 2024 | Sep 25, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Dec 2023 | 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.
- Llama-3.2-11B-Vision-Instruct$2.99
- Llama-3.2-1B$0.936
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.2-11B-Vision-Instruct, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features. Llama-3.2-1B wins 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, Llama-3.2-11B-Vision-Instruct, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Llama-3.2-1B is cheaper at $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); Llama-3.2-11B-Vision-Instruct costs $0.197 input / $0.51 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.085 per million tokens for Llama-3.2-1B versus $0.10 for Qwen2.5-Coder-0.5B (1.2× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (3.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-11B-Vision-Instruct has not been scored yet, Llama-3.2-1B has an ECI of 102.0 and Qwen2.5-Coder-0.5B has an ECI of 88.2.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-11B-Vision-Instruct, Llama-3.2-1B and Qwen2.5-Coder-0.5B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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?
Llama-3.2-1B has the largest context window at 131,072 tokens, against 128,000 for Llama-3.2-11B-Vision-Instruct and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Llama-3.2-11B-Vision-Instruct up to 4,096, 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?
Llama-3.2-11B-Vision-Instruct accepts text and images; Llama-3.2-1B accepts text; Qwen2.5-Coder-0.5B accepts text. Llama-3.2-11B-Vision-Instruct 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?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, 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.
