o3-deep-research vs GLM-4.5V vs Gemma 3n E2B IT
Too close to call on our weighted score (o3-deep-research 49, GLM-4.5V 47, Gemma 3n E2B IT 21). The right pick depends on what you value most.
OpenAIo3-deep-research
49/100- ECI—
- Price$9.00 / $36.00
- Context200K
Z.ai (Zhipu)GLM-4.5V
47/100- ECI—
- Price$0.60 / $1.80
- Context64K
GoogleGemma 3n E2B IT
21/100- ECI—
- Price—
- Context33K
Too close to call
It is close. Our weighted score puts them within 2 points (o3-deep-research 49/100, GLM-4.5V 47/100, Gemma 3n E2B IT 21/100), so choose by what matters most for your work: GLM-4.5V on price and o3-deep-research for long inputs. 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 priceGLM-4.5VGLM-4.5V $0.90 · o3-deep-research $15.75 per 1M tokens (3:1 blend) · Gemma 3n E2B IT unpriced
- Longest contexto3-deep-researcho3-deep-research 200,000 · GLM-4.5V 64,000 · Gemma 3n E2B IT 32,768 tokens
- Widest inputsGLM-4.5V and Gemma 3n E2B ITo3-deep-research: Text, Images · GLM-4.5V: Text, Images, Video · Gemma 3n E2B IT: Text, Images, Audio
- Self-hostingGLM-4.5V and Gemma 3n E2B ITPublishes downloadable weights (Gemma Terms of Use)
| Measure | Weight | o3-deep-research | GLM-4.5V | Gemma 3n E2B IT |
|---|---|---|---|---|
| Inputs & features | 60% | 60 | 70 | 35 |
| Context window | 40% | 32 | 12 | 0 |
| Overall | 100% | 49/100 | 47/100 | 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 | GLM-4.5VZ.ai (Zhipu) | Gemma 3n E2B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $9.00 | $0.60 (best) | — |
| Output | $36.00 | $1.80 (best) | — |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $15.75 | $0.90 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Median of 1 providers | Official Z.AI API | — |
| Limits | |||
| Context window | 200,000 tokens (best) | 64,000 tokens | 32,768 tokens |
| Max output | 100,000 tokens (best) | 16,384 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenGemma Terms of Use |
| API model ID | — | glm-4.5v | — |
| API providers | 1 | 17 (best) | — |
| Released | Jun 26, 2025 | Aug 11, 2025 | Jun 26, 2025 |
| Knowledge cutoff | May 2024 | Apr 2025 | 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
- GLM-4.5V$9.60
- Gemma 3n E2B IT—
Which should you choose?
Which is better: o3-deep-research, GLM-4.5V or Gemma 3n E2B IT?
It is close. Our weighted score puts them within 2 points (o3-deep-research 49/100, GLM-4.5V 47/100, Gemma 3n E2B IT 21/100), so choose by what matters most for your work: GLM-4.5V on price and o3-deep-research for long inputs. 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, GLM-4.5V or Gemma 3n E2B IT?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). 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 $0.90 per million tokens for GLM-4.5V versus $15.75 for o3-deep-research (18× 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, GLM-4.5V has not been scored yet 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, GLM-4.5V 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 64,000 for GLM-4.5V and 32,768 for Gemma 3n E2B IT. Maximum output per response: o3-deep-research up to 100,000, GLM-4.5V up to 16,384, Gemma 3n E2B IT up to 32,768 tokens.
Which can read images, PDFs, audio or video?
o3-deep-research accepts text and images; GLM-4.5V accepts text, images and video; Gemma 3n E2B IT accepts text, images and audio. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V 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?
GLM-4.5V is the newest, released Aug 11, 2025. o3-deep-research came out Jun 26, 2025; Gemma 3n E2B IT came out Jun 26, 2025. Knowledge cutoff: o3-deep-research May 2024, GLM-4.5V Apr 2025, 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.
