Llama-3.2-1B vs Qwen Turbo vs Qwen2.5-Coder-0.5B
Qwen Turbo comes out ahead, 72 to 55 and 49 on our weighted score.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
- Our pick
Alibaba (Qwen)Qwen Turbo
72/100- ECI—
- Price$0.05 / $0.20
- Context1M
- Alibaba (Qwen)
Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Qwen Turbo is our pick
Qwen Turbo is the better all-round choice, scoring 72/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features and context window. 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 · Qwen Turbo $0.087 · Qwen2.5-Coder-0.5B $0.10 per 1M tokens (3:1 blend)
- Longest contextQwen TurboQwen Turbo 1,000,000 · Llama-3.2-1B 131,072 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Qwen Turbo: Text · Qwen2.5-Coder-0.5B: Text
- Self-hostingLlama-3.2-1B and Qwen2.5-Coder-0.5BPublishes downloadable weights (Llama 3.2 Community License and Apache 2.0)
| Measure | Weight | Llama-3.2-1B | Qwen Turbo | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 97 |
| Inputs & features | 30% | 0 | 35 | 0 |
| Context window | 20% | 24 | 60 | 0 |
| Overall | 100% | 55/100 | 72/100 | 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-3.2-1BMeta | Qwen TurboAlibaba (Qwen) | 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% | 41.8% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | 6.1% (best) | — |
| Price per million tokens | |||
| Input | $0.064 | $0.05 (best) | $0.10 |
| Output | $0.15 | $0.20 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.087 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 32,768 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) | 8,192 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 | Yes | No |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Proprietary | OpenApache 2.0 |
| API model ID | — | qwen-turbo | — |
| API providers | 2 | 3 (best) | 1 |
| Released | Sep 25, 2024 | Nov 1, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Dec 2023 | Apr 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$0.936
- Qwen Turbo$0.90
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.2-1B, Qwen Turbo or Qwen2.5-Coder-0.5B?
Qwen Turbo is the better all-round choice, scoring 72/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features and context window. 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-3.2-1B, Qwen Turbo 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). Qwen Turbo costs $0.05 input / $0.20 output per million tokens (official Alibaba API price); 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 $0.085 per million tokens for Llama-3.2-1B versus $0.087 for Qwen Turbo (1× as much) and $0.10 for Qwen2.5-Coder-0.5B (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Qwen Turbo has not been scored yet 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-3.2-1B, Qwen Turbo 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?
Qwen Turbo has the largest context window at 1,000,000 tokens, against 131,072 for Llama-3.2-1B and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Llama-3.2-1B up to 8,192, Qwen Turbo up to 16,384, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Qwen Turbo accepts text; Qwen2.5-Coder-0.5B accepts text. They handle the same number of input types.
Are any of these open source?
Llama-3.2-1B and Qwen2.5-Coder-0.5B publishes its weights (Llama 3.2 Community License and Apache 2.0) and can be self-hosted; Qwen Turbo is proprietary.
Which is newer?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Qwen Turbo came out Nov 1, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Qwen Turbo Apr 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 Oct 4, 2026.
