o3 vs Llama-3.2-3B vs Gemini 2.5 Pro
Gemini 2.5 Pro comes out ahead, 54 to 49 and 42 on our weighted score, though Llama-3.2-3B is 22× cheaper per token.
OpenAIo3
42/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
MetaLlama-3.2-3B
49/100- ECI—
- Price$0.10 / $0.335
- Context131K
- Our pick
GoogleGemini 2.5 Pro
54/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 54/100 against Llama-3.2-3B (49) and o3 (42). It leads on inputs & features and context window. Llama-3.2-3B 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-3.2-3BLlama-3.2-3B $0.159 · Gemini 2.5 Pro $3.44 · o3 $3.50 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · o3 200,000 · Llama-3.2-3B 131,072 tokens
- Widest inputsGemini 2.5 Proo3: Text, Images, PDFs · Llama-3.2-3B: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video
- Self-hostingLlama-3.2-3BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | o3 | Llama-3.2-3B | Gemini 2.5 Pro |
|---|---|---|---|---|
| Price | 50% | 24 | 88 | 24 |
| Inputs & features | 30% | 80 | 0 | 100 |
| Context window | 20% | 32 | 24 | 61 |
| Overall | 100% | 42/100 | 49/100 | 54/100 |
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 | o3OpenAI | Llama-3.2-3BMeta | Gemini 2.5 ProGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.9 (best) | — | 145.3 |
| ECI rank | #63 of 148 (best) | — | #78 of 148 |
| GPQA DiamondGraduate-level science questions | 81.8% | — | 85.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 33.3% (best) | — | 24.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.4% | — | 84.7% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 62.3% (best) | — | 57.6% |
| SimpleQA VerifiedShort factual questions | 49.4% | — | — |
| Price per million tokens | |||
| Input | $2.00 | $0.10 (best) | $1.25 |
| Output | $8.00 | $0.335 (best) | $10.00 |
| Cached input | $0.50 | — | $0.125 (best) |
| Blended (3:1) | $3.50 | $0.159 (best) | $3.44 |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $15.00 |
| Price source | Official OpenAI API | Median of 3 providers | Official Google API |
| Limits | |||
| Context window | 200,000 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 100,000 tokens (best) | 8,192 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenLlama 3.2 Community License | Proprietary |
| API model ID | o3 | — | gemini-2.5-pro |
| API providers | 18 | 3 | 22 (best) |
| Released | Apr 16, 2025 | Sep 25, 2024 | Jun 17, 2025 |
| Knowledge cutoff | May 2024 | Dec 2023 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3$36.00
- Llama-3.2-3B$1.67
- Gemini 2.5 Pro$32.50
Which should you choose?
Which is better: o3, Llama-3.2-3B or Gemini 2.5 Pro?
Gemini 2.5 Pro is the better all-round choice, scoring 54/100 against Llama-3.2-3B (49) and o3 (42). It leads on inputs & features and context window. Llama-3.2-3B 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, o3, Llama-3.2-3B or Gemini 2.5 Pro?
Llama-3.2-3B is cheaper at $0.10 input / $0.335 output per million tokens (median across 3 API providers). Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price); o3 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.159 per million tokens for Llama-3.2-3B versus $3.44 for Gemini 2.5 Pro (22× as much) and $3.50 for o3 (22× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o3 has an ECI of 146.9, Llama-3.2-3B has not been scored yet and Gemini 2.5 Pro has an ECI of 145.3.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-3B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-3B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 200,000 for o3 and 131,072 for Llama-3.2-3B. Maximum output per response: o3 up to 100,000, Llama-3.2-3B up to 8,192, Gemini 2.5 Pro up to 65,536 tokens.
Which can read images, PDFs, audio or video?
o3 accepts text, images and PDFs; Llama-3.2-3B accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
Llama-3.2-3B publishes its weights (Llama 3.2 Community License) and can be self-hosted; o3 and Gemini 2.5 Pro is proprietary.
Which is newer?
Gemini 2.5 Pro is the newest, released Jun 17, 2025. o3 came out Apr 16, 2025; Llama-3.2-3B came out Sep 25, 2024. Knowledge cutoff: o3 May 2024, Llama-3.2-3B Dec 2023, Gemini 2.5 Pro Jan 2025.
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.
