GLM-4.5V vs Jamba Large vs o4-mini-deep-research
GLM-4.5V comes out ahead, 48 to 37 and 29 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.5V
48/100- ECI—
- Price$0.60 / $1.80
- Context64K
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 48/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price and inputs & features. Jamba Large wins on context window. 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 priceGLM-4.5VGLM-4.5V $0.90 · o4-mini-deep-research $3.15 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextJamba LargeJamba Large 256,000 · o4-mini-deep-research 200,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Jamba Large: Text · o4-mini-deep-research: Text, Images
- Self-hostingGLM-4.5V and Jamba LargePublishes downloadable weights
| Measure | Weight | GLM-4.5V | Jamba Large | o4-mini-deep-research |
|---|---|---|---|---|
| Price | 50% | 50 | 23 | 25 |
| Inputs & features | 30% | 70 | 35 | 60 |
| Context window | 20% | 12 | 36 | 32 |
| Overall | 100% | 48/100 | 29/100 | 37/100 |
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 | GLM-4.5VZ.ai (Zhipu) | Jamba LargeAI21 Labs | o4-mini-deep-researchOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 (best) | $2.00 | $1.80 |
| Output | $1.80 (best) | $8.00 | $7.20 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $0.90 (best) | $3.50 | $3.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official AI21 Labs API | Median of 1 providers |
| Limits | |||
| Context window | 64,000 tokens | 256,000 tokens (best) | 200,000 tokens |
| Max output | 16,384 tokens | 4,096 tokens | 100,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-4.5v | jamba-large | — |
| API providers | 17 (best) | 1 | 1 |
| Released | Aug 11, 2025 | Jul 3, 2025 | Jun 26, 2025 |
| Knowledge cutoff | Apr 2025 | Aug 22, 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.
- GLM-4.5V$9.60
- Jamba Large$36.00
- o4-mini-deep-research$32.40
Which should you choose?
Which is better: GLM-4.5V, Jamba Large or o4-mini-deep-research?
GLM-4.5V is the better all-round choice, scoring 48/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price and inputs & features. Jamba Large wins on context window. 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, GLM-4.5V, Jamba Large or o4-mini-deep-research?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). o4-mini-deep-research costs $1.80 input / $7.20 output per million tokens (median across 1 API provider); Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs API price). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $3.15 for o4-mini-deep-research (3.5× as much) and $3.50 for Jamba Large (3.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Jamba Large has not been scored yet and o4-mini-deep-research has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Jamba Large and o4-mini-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?
Jamba Large has the largest context window at 256,000 tokens, against 200,000 for o4-mini-deep-research and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Jamba Large up to 4,096, o4-mini-deep-research up to 100,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Jamba Large accepts text; o4-mini-deep-research accepts text and images. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V and Jamba Large publishes its weights and can be self-hosted; o4-mini-deep-research is proprietary.
Which is newer?
GLM-4.5V is the newest, released Aug 11, 2025. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Jamba Large Aug 22, 2024, o4-mini-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.
