Llama-3.2-1B vs Nova Lite vs Qwen2.5-Coder-0.5B
Nova Lite comes out ahead, 77 to 55 and 49 on our weighted score, though Llama-3.2-1B is 19% cheaper per token.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
- Our pick
AmazonNova Lite
77/100- ECI—
- Price$0.06 / $0.24
- Context300K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Nova Lite is our pick
Nova Lite is the better all-round choice, scoring 77/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features and context window. 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 · Nova Lite $0.105 per 1M tokens (3:1 blend)
- Longest contextNova LiteNova Lite 300,000 · Llama-3.2-1B 131,072 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsNova LiteLlama-3.2-1B: Text · Nova Lite: Text, Images, PDFs, Video · 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 | Nova Lite | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 100 | 96 | 97 |
| Inputs & features | 30% | 0 | 70 | 0 |
| Context window | 20% | 24 | 39 | 0 |
| Overall | 100% | 55/100 | 77/100 | 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 | Nova LiteAmazon | 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 | $0.064 | $0.06 (best) | $0.10 |
| Output | $0.15 | $0.24 | $0.10 (best) |
| Cached input | — | $0.015 | — |
| Blended (3:1) | $0.085 (best) | $0.105 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Amazon Bedrock API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 300,000 tokens (best) | 32,768 tokens |
| Max output | 8,192 tokens | 10,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | No | 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 | — | amazon.nova-lite-v1:0 | — |
| API providers | 2 | 3 (best) | 1 |
| Released | Sep 25, 2024 | Dec 3, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Dec 2023 | Oct 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
- Nova Lite$1.08
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.2-1B, Nova Lite or Qwen2.5-Coder-0.5B?
Nova Lite is the better all-round choice, scoring 77/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads on inputs & features and context window. 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-3.2-1B, Nova Lite 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); Nova Lite costs $0.06 input / $0.24 output per million tokens (official Amazon Bedrock API price). 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 $0.105 for Nova Lite (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, Nova Lite 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, Nova Lite 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?
Nova Lite has the largest context window at 300,000 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, Nova Lite up to 10,000, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Nova Lite accepts text, images, PDFs and video; Qwen2.5-Coder-0.5B accepts text. Nova Lite handles the widest range of inputs.
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; Nova Lite is proprietary.
Which is newer?
Nova Lite is the newest, released Dec 3, 2024. Qwen2.5-Coder-0.5B came out Nov 12, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Nova Lite Oct 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.
