o3-deep-research vs Llama-3.1-8B-Instruct vs Gemma 3n E2B IT
o3-deep-research comes out ahead, 49 to 25 and 21 on our weighted score, though Llama-3.1-8B-Instruct is 242× cheaper per token.
- Our pick
OpenAIo3-deep-research
49/100- ECI—
- Price$9.00 / $36.00
- Context200K
MetaLlama-3.1-8B-Instruct
25/100- ECI116.4
- Price$0.06 / $0.08
- Context128K
GoogleGemma 3n E2B IT
21/100- ECI—
- Price—
- Context33K
o3-deep-research is our pick
o3-deep-research is the better all-round choice, scoring 49/100 against Llama-3.1-8B-Instruct (25) and Gemma 3n E2B 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 priceLlama-3.1-8B-InstructLlama-3.1-8B-Instruct $0.065 · o3-deep-research $15.75 per 1M tokens (3:1 blend) · Gemma 3n E2B IT unpriced
- Longest contexto3-deep-researcho3-deep-research 200,000 · Llama-3.1-8B-Instruct 128,000 · Gemma 3n E2B IT 32,768 tokens
- Widest inputsGemma 3n E2B ITo3-deep-research: Text, Images · Llama-3.1-8B-Instruct: Text · Gemma 3n E2B IT: Text, Images, Audio
- Self-hostingLlama-3.1-8B-Instruct and Gemma 3n E2B ITPublishes downloadable weights (Gemma Terms of Use)
| Measure | Weight | o3-deep-research | Llama-3.1-8B-Instruct | Gemma 3n E2B IT |
|---|---|---|---|---|
| Inputs & features | 60% | 60 | 25 | 35 |
| Context window | 40% | 32 | 24 | 0 |
| Overall | 100% | 49/100 | 25/100 | 21/100 |
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 | o3-deep-researchOpenAI | Llama-3.1-8B-InstructMeta | Gemma 3n E2B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 116.4 | — |
| ECI rank | — | #162 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 27.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.7% | — |
| Price per million tokens | |||
| Input | $9.00 | $0.06 (best) | — |
| Output | $36.00 | $0.08 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $15.75 | $0.065 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Median of 1 providers | Median of 17 providers | — |
| Limits | |||
| Context window | 200,000 tokens (best) | 128,000 tokens | 32,768 tokens |
| Max output | 100,000 tokens (best) | 4,096 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenGemma Terms of Use |
| API model ID | — | — | — |
| API providers | 1 | 18 (best) | — |
| Released | Jun 26, 2025 | Jul 23, 2024 | Jun 26, 2025 |
| Knowledge cutoff | May 2024 | Dec 2023 | Jun 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3-deep-research$162.00
- Llama-3.1-8B-Instruct$0.76
- Gemma 3n E2B IT—
Which should you choose?
Which is better: o3-deep-research, Llama-3.1-8B-Instruct or Gemma 3n E2B IT?
o3-deep-research is the better all-round choice, scoring 49/100 against Llama-3.1-8B-Instruct (25) and Gemma 3n E2B 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, o3-deep-research, Llama-3.1-8B-Instruct or Gemma 3n E2B IT?
Llama-3.1-8B-Instruct is cheaper at $0.06 input / $0.08 output per million tokens (median across 17 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 $0.065 per million tokens for Llama-3.1-8B-Instruct versus $15.75 for o3-deep-research (242× as much). Gemma 3n E2B IT has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o3-deep-research has not been scored yet, Llama-3.1-8B-Instruct has an ECI of 116.4 and Gemma 3n E2B IT has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o3-deep-research, Llama-3.1-8B-Instruct and Gemma 3n E2B IT yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Gemma 3n E2B 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 128,000 for Llama-3.1-8B-Instruct and 32,768 for Gemma 3n E2B IT. Maximum output per response: o3-deep-research up to 100,000, Llama-3.1-8B-Instruct up to 4,096, Gemma 3n E2B IT up to 32,768 tokens.
Which can read images, PDFs, audio or video?
o3-deep-research accepts text and images; Llama-3.1-8B-Instruct accepts text; Gemma 3n E2B IT accepts text, images and audio. Gemma 3n E2B IT handles the widest range of inputs.
Are any of these open source?
Llama-3.1-8B-Instruct and Gemma 3n E2B IT publishes its weights (Gemma Terms of Use) and can be self-hosted; o3-deep-research is proprietary.
Which is newer?
o3-deep-research is the newest, released Jun 26, 2025. Gemma 3n E2B IT came out Jun 26, 2025; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: o3-deep-research May 2024, Llama-3.1-8B-Instruct Dec 2023, Gemma 3n E2B IT Jun 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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