Gemini 2.5 Pro vs Llama-3.2-3B vs o3
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.
- Our pick
GoogleGemini 2.5 Pro
54/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
MetaLlama-3.2-3B
49/100- ECI—
- Price$0.10 / $0.335
- Context131K
OpenAIo3
42/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
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 ProGemini 2.5 Pro: Text, Images, PDFs, Audio, Video · Llama-3.2-3B: Text · o3: Text, Images, PDFs
- Self-hostingLlama-3.2-3BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | Gemini 2.5 Pro | Llama-3.2-3B | o3 |
|---|---|---|---|---|
| Price | 50% | 24 | 88 | 24 |
| Inputs & features | 30% | 100 | 0 | 80 |
| Context window | 20% | 61 | 24 | 32 |
| Overall | 100% | 54/100 | 49/100 | 42/100 |
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 | Gemini 2.5 ProGoogle | Llama-3.2-3BMeta | o3OpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.3 | — | 146.9 (best) |
| ECI rank | #78 of 148 | — | #63 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 85.3% (best) | — | 81.8% |
| FrontierMath Tiers 1–3Research-level mathematics | 24.6% | — | 33.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.7% (best) | — | 84.4% |
| SWE-bench VerifiedFixing real GitHub issues | 57.6% | — | 62.3% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 49.4% |
| Price per million tokens | |||
| Input | $1.25 | $0.10 (best) | $2.00 |
| Output | $10.00 | $0.335 (best) | $8.00 |
| Cached input | $0.125 (best) | — | $0.50 |
| Blended (3:1) | $3.44 | $0.159 (best) | $3.50 |
| Long-context rate | Over 200K: $2.50 / $15.00 | Same rate | Same rate |
| Price source | Official Google API | Median of 3 providers | Official OpenAI API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 131,072 tokens | 200,000 tokens |
| Max output | 65,536 tokens | 8,192 tokens | 100,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yeslow · medium · high |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenLlama 3.2 Community License | Proprietary |
| API model ID | gemini-2.5-pro | — | o3 |
| API providers | 22 (best) | 3 | 18 |
| Released | Jun 17, 2025 | Sep 25, 2024 | Apr 16, 2025 |
| Knowledge cutoff | Jan 2025 | Dec 2023 | May 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.5 Pro$32.50
- Llama-3.2-3B$1.67
- o3$36.00
Which should you choose?
Which is better: Gemini 2.5 Pro, Llama-3.2-3B or o3?
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, Gemini 2.5 Pro, Llama-3.2-3B or o3?
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. Gemini 2.5 Pro has an ECI of 145.3, Llama-3.2-3B has not been scored yet and o3 has an ECI of 146.9.
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: Gemini 2.5 Pro up to 65,536, Llama-3.2-3B up to 8,192, o3 up to 100,000 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Pro accepts text, images, PDFs, audio and video; Llama-3.2-3B accepts text; o3 accepts text, images and PDFs. 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; Gemini 2.5 Pro and o3 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: Gemini 2.5 Pro Jan 2025, Llama-3.2-3B Dec 2023, o3 May 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.
