Gemma 3n E4B IT vs Qwen2.5 7B Instruct vs o3-deep-research
o3-deep-research comes out ahead, 49 to 25 and 21 on our weighted score, though Qwen2.5 7B Instruct is 252× cheaper per token.
GoogleGemma 3n E4B IT
21/100- ECI—
- Price—
- Context33K
Alibaba (Qwen)Qwen2.5 7B Instruct
25/100- ECI118.5
- Price$0.05 / $0.10
- Context131K
- Our pick
OpenAIo3-deep-research
49/100- ECI—
- Price$9.00 / $36.00
- Context200K
o3-deep-research is our pick
o3-deep-research is the better all-round choice, scoring 49/100 against Qwen2.5 7B Instruct (25) and Gemma 3n E4B IT (21). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · o3-deep-research $15.75 per 1M tokens (3:1 blend) · Gemma 3n E4B IT unpriced
- Longest contexto3-deep-researcho3-deep-research 200,000 · Qwen2.5 7B Instruct 131,072 · Gemma 3n E4B IT 32,768 tokens
- Widest inputsGemma 3n E4B ITGemma 3n E4B IT: Text, Images, Audio · Qwen2.5 7B Instruct: Text · o3-deep-research: Text, Images
- Self-hostingGemma 3n E4B IT and Qwen2.5 7B InstructPublishes downloadable weights (Gemma Terms of Use and Apache 2.0)
| Measure | Weight | Gemma 3n E4B IT | Qwen2.5 7B Instruct | o3-deep-research |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 25 | 60 |
| Context window | 40% | 0 | 25 | 32 |
| Overall | 100% | 21/100 | 25/100 | 49/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemma 3n E4B ITGoogle | Qwen2.5 7B InstructAlibaba (Qwen) | o3-deep-researchOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 118.5 | — |
| ECI rank | — | #159 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 35.5% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 2.5% | — |
| Price per million tokens | |||
| Input | — | $0.05 (best) | $9.00 |
| Output | — | $0.10 (best) | $36.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.063 (best) | $15.75 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 3 providers | Median of 1 providers |
| Limits | |||
| Context window | 32,768 tokens | 131,072 tokens | 200,000 tokens (best) |
| Max output | 32,768 tokens | 8,192 tokens | 100,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenGemma Terms of Use | OpenApache 2.0 | Proprietary |
| API model ID | — | — | — |
| API providers | — | 4 (best) | 1 |
| Released | Jun 26, 2025 | Sep 19, 2024 | Jun 26, 2025 |
| Knowledge cutoff | Jun 2024 | — | 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.
- Gemma 3n E4B IT—
- Qwen2.5 7B Instruct$0.70
- o3-deep-research$162.00
Which should you choose?
Which is better: Gemma 3n E4B IT, Qwen2.5 7B Instruct or o3-deep-research?
o3-deep-research is the better all-round choice, scoring 49/100 against Qwen2.5 7B Instruct (25) and Gemma 3n E4B IT (21). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Gemma 3n E4B IT, Qwen2.5 7B Instruct or o3-deep-research?
Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). o3-deep-research costs $9.00 input / $36.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Qwen2.5 7B Instruct versus $15.75 for o3-deep-research (252× as much). Gemma 3n E4B IT has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma 3n E4B IT has not been scored yet, Qwen2.5 7B Instruct has an ECI of 118.5 and o3-deep-research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 3n E4B IT, Qwen2.5 7B Instruct and o3-deep-research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Gemma 3n E4B IT does not support tool calling, which most coding agents need.
Which has the bigger context window?
o3-deep-research has the largest context window at 200,000 tokens, against 131,072 for Qwen2.5 7B Instruct and 32,768 for Gemma 3n E4B IT. Maximum output per response: Gemma 3n E4B IT up to 32,768, Qwen2.5 7B Instruct up to 8,192, o3-deep-research up to 100,000 tokens.
Which can read images, PDFs, audio or video?
Gemma 3n E4B IT accepts text, images and audio; Qwen2.5 7B Instruct accepts text; o3-deep-research accepts text and images. Gemma 3n E4B IT handles the widest range of inputs.
Are any of these open source?
Gemma 3n E4B IT and Qwen2.5 7B Instruct publishes its weights (Gemma Terms of Use and Apache 2.0) and can be self-hosted; o3-deep-research is proprietary.
Which is newer?
Gemma 3n E4B IT is the newest, released Jun 26, 2025. o3-deep-research came out Jun 26, 2025; Qwen2.5 7B Instruct came out Sep 19, 2024. Knowledge cutoff: Gemma 3n E4B IT Jun 2024, o3-deep-research 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 Nov 2, 2026.
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