GLM-4.5-Flash vs Nemotron Mini 4B Instruct vs o3-deep-research
o3-deep-research comes out ahead, 49 to 31 and 25 on our weighted score.
Z.ai (Zhipu)GLM-4.5-Flash
31/100- ECI—
- PriceFree / Free
- Context131K
NVIDIANemotron Mini 4B Instruct
25/100- ECI—
- PriceFree / Free
- Context128K
- Our pick
OpenAIo3-deep-research
49/100- ECI—
- Price—
- Context200K
o3-deep-research is our pick
o3-deep-research is the better all-round choice, scoring 49/100 against GLM-4.5-Flash (31) and Nemotron Mini 4B Instruct (25). 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 priceGLM-4.5-Flash and Nemotron Mini 4B InstructGLM-4.5-Flash Free · Nemotron Mini 4B Instruct Free per 1M tokens (3:1 blend) · o3-deep-research unpriced
- Longest contexto3-deep-researcho3-deep-research 200,000 · GLM-4.5-Flash 131,072 · Nemotron Mini 4B Instruct 128,000 tokens
- Widest inputso3-deep-researchGLM-4.5-Flash: Text · Nemotron Mini 4B Instruct: Text · o3-deep-research: Text, Images
- Self-hostingNemotron Mini 4B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Nemotron Mini 4B Instruct | o3-deep-research |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 25 | 60 |
| Context window | 40% | 24 | 24 | 32 |
| Overall | 100% | 31/100 | 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 | GLM-4.5-FlashZ.ai (Zhipu) | Nemotron Mini 4B InstructNVIDIA | o3-deep-researchOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free | Free | — |
| Output | Free | Free | — |
| Cached input | — | — | — |
| Blended (3:1) | Free | Free | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official Z.AI API | Official Nvidia API | — |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 200,000 tokens (best) |
| Max output | 98,304 tokens | 8,192 tokens | 100,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | glm-4.5-flash | nvidia/nemotron-mini-4b-instruct | — |
| API providers | 4 (best) | 1 | — |
| Released | Jul 28, 2025 | Aug 21, 2024 | Jun 26, 2024 |
| Knowledge cutoff | Apr 2025 | — | 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.
- GLM-4.5-FlashFree
- Nemotron Mini 4B InstructFree
- o3-deep-research—
Which should you choose?
Which is better: GLM-4.5-Flash, Nemotron Mini 4B Instruct or o3-deep-research?
o3-deep-research is the better all-round choice, scoring 49/100 against GLM-4.5-Flash (31) and Nemotron Mini 4B Instruct (25). 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, GLM-4.5-Flash, Nemotron Mini 4B Instruct or o3-deep-research?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Nemotron Mini 4B Instruct costs Free input / Free output per million tokens (official Nvidia API price). GLM-4.5-Flash is listed as free. o3-deep-research has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5-Flash has not been scored yet, Nemotron Mini 4B Instruct has not been scored yet and o3-deep-research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Nemotron Mini 4B Instruct and o3-deep-research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
o3-deep-research has the largest context window at 200,000 tokens, against 131,072 for GLM-4.5-Flash and 128,000 for Nemotron Mini 4B Instruct. Maximum output per response: GLM-4.5-Flash up to 98,304, Nemotron Mini 4B Instruct up to 8,192, o3-deep-research up to 100,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Nemotron Mini 4B Instruct accepts text; o3-deep-research accepts text and images. o3-deep-research handles the widest range of inputs.
Are any of these open source?
Nemotron Mini 4B Instruct publishes its weights and can be self-hosted; GLM-4.5-Flash and o3-deep-research is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Nemotron Mini 4B Instruct came out Aug 21, 2024; o3-deep-research came out Jun 26, 2024. Knowledge cutoff: GLM-4.5-Flash Apr 2025, 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 Oct 4, 2026.
