Qwen2.5-Coder-0.5B vs Llama-3.1-8B-Instruct vs Llama-3.2-1B
Llama-3.1-8B-Instruct comes out ahead, 46 to 36 and 24 on our weighted score, though Llama-3.2-1B is 45% cheaper per token.
Alibaba (Qwen)Qwen2.5-Coder-0.5B
24/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
- Our pick
MetaLlama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
- Meta
Llama-3.2-1B
36/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Llama-3.1-8B-Instruct is our pick
Llama-3.1-8B-Instruct is the better all-round choice, scoring 46/100 against Llama-3.2-1B (36) and Qwen2.5-Coder-0.5B (24). It leads on capability and inputs & features. Llama-3.2-1B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityLlama-3.1-8B-InstructCapabilities Index (ECI): Llama-3.1-8B-Instruct 116.6 · Llama-3.2-1B 102.0 · Qwen2.5-Coder-0.5B 88.2
- Lowest priceLlama-3.2-1BLlama-3.2-1B $0.085 · Qwen2.5-Coder-0.5B $0.10 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Llama-3.1-8B-Instruct 128,000 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsSame inputsQwen2.5-Coder-0.5B: Text · Llama-3.1-8B-Instruct: Text · Llama-3.2-1B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-Coder-0.5B | Llama-3.1-8B-Instruct | Llama-3.2-1B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 0 | 36 | 17 |
| Price | 25% | 97 | 88 | 100 |
| Inputs & features | 15% | 0 | 25 | 0 |
| Context window | 10% | 0 | 24 | 24 |
| Overall | 100% | 24/100 | 46/100 | 36/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen2.5-Coder-0.5BAlibaba (Qwen) | Llama-3.1-8B-InstructMeta | Llama-3.2-1BMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 88.2 | 116.6 (best) | 102.0 |
| ECI rank | #148 of 148 | #145 of 148 (best) | #147 of 148 |
| GPQA DiamondGraduate-level science questions | — | 27.0% (best) | 23.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% (best) | 0.6% |
| Price per million tokens | |||
| Input | $0.10 | $0.152 | $0.064 (best) |
| Output | $0.10 (best) | $0.167 | $0.15 |
| Cached input | — | — | — |
| Blended (3:1) | $0.10 | $0.156 | $0.085 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 9 providers | Median of 2 providers |
| Limits | |||
| Context window | 32,768 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens (best) | 4,096 tokens | 8,192 tokens (best) |
| 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 | No | No |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | OpenLlama 3.2 Community License |
| API model ID | — | — | — |
| API providers | 1 | 9 (best) | 2 |
| Released | Nov 12, 2024 | Jul 23, 2024 | Sep 25, 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.
- Qwen2.5-Coder-0.5B$1.20
- Llama-3.1-8B-Instruct$1.85
- Llama-3.2-1B$0.936
Which should you choose?
Which is better: Qwen2.5-Coder-0.5B, Llama-3.1-8B-Instruct or Llama-3.2-1B?
Llama-3.1-8B-Instruct is the better all-round choice, scoring 46/100 against Llama-3.2-1B (36) and Qwen2.5-Coder-0.5B (24). It leads on capability and inputs & features. Llama-3.2-1B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen2.5-Coder-0.5B, Llama-3.1-8B-Instruct or Llama-3.2-1B?
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-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 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 $0.156 for Llama-3.1-8B-Instruct (1.8× as much).
Which scores higher on benchmarks?
Llama-3.1-8B-Instruct scores higher on the Capabilities Index (ECI): Llama-3.1-8B-Instruct 116.6 (#145 of 148), Llama-3.2-1B 102.0 (#147 of 148) and Qwen2.5-Coder-0.5B 88.2 (#148 of 148). The confidence ranges of the top two overlap (106.3–121.6 vs 90.7–110.3), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-Coder-0.5B, Llama-3.1-8B-Instruct and Llama-3.2-1B yet, so there is no like-for-like coding score. On overall capability, Llama-3.1-8B-Instruct leads, which tends to carry over to coding, but test on your own codebase. Note that Qwen2.5-Coder-0.5B and Llama-3.2-1B 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-3.1-8B-Instruct and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Qwen2.5-Coder-0.5B up to 8,192, Llama-3.1-8B-Instruct up to 4,096, Llama-3.2-1B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-0.5B accepts text; Llama-3.1-8B-Instruct accepts text; Llama-3.2-1B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0 and Llama 3.2 Community License), so you can self-host them.
Which is newer?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Llama-3.2-1B came out Sep 25, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Llama-3.1-8B-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.
