Llama-3.2-1B vs Phi-4-mini vs Qwen2.5-Coder-0.5B
Phi-4-mini comes out ahead, 58 to 55 and 49 on our weighted score, though Llama-3.2-1B is 35% cheaper per token.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
- Our pick
MicrosoftPhi-4-mini
58/100- ECI—
- Price$0.075 / $0.30
- Context128K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Phi-4-mini is our pick
Phi-4-mini 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 · Phi-4-mini $0.131 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Phi-4-mini 128,000 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Phi-4-mini: 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-3.2-1B | Phi-4-mini | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 100 | 92 | 97 |
| Inputs & features | 30% | 0 | 25 | 0 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 55/100 | 58/100 | 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 | Phi-4-miniMicrosoft | 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 (best) | $0.075 | $0.10 |
| Output | $0.15 | $0.30 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.131 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Azure API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 32,768 tokens |
| 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 | OpenLlama 3.2 Community License | Open | OpenApache 2.0 |
| API model ID | — | phi-4-mini | — |
| API providers | 2 (best) | 1 | 1 |
| Released | Sep 25, 2024 | Dec 11, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Dec 2023 | Oct 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-3.2-1B$0.936
- Phi-4-mini$1.35
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.2-1B, Phi-4-mini or Qwen2.5-Coder-0.5B?
Phi-4-mini 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-3.2-1B, Phi-4-mini 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); Phi-4-mini costs $0.075 input / $0.30 output per million tokens (official Azure 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 $0.131 for Phi-4-mini (1.5× 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, Phi-4-mini 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, Phi-4-mini 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 Phi-4-mini and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Llama-3.2-1B up to 8,192, Phi-4-mini up to 4,096, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Phi-4-mini accepts text; Qwen2.5-Coder-0.5B accepts text. They handle the same number of input types.
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?
Phi-4-mini is the newest, released Dec 11, 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, Phi-4-mini Oct 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.
