o4-mini-deep-research vs Gemini 4 Argon vs Jamba Large
Gemini 4 Argon comes out ahead, 52 to 37 and 29 on our weighted score, though o4-mini-deep-research is 21% cheaper per token.
OpenAIo4-mini-deep-research
37/100- ECI—
- Price$1.80 / $7.20
- Context200K
- Our pick
GoogleGemini 4 Argon
52/100- ECI166.1
- Price$2.00 / $10.00
- Context1.05M
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
Gemini 4 Argon is our pick
Gemini 4 Argon is the better all-round choice, scoring 52/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on inputs & features and context window. o4-mini-deep-research wins on price. 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 priceo4-mini-deep-researcho4-mini-deep-research $3.15 · Jamba Large $3.50 · Gemini 4 Argon $4.00 per 1M tokens (3:1 blend)
- Longest contextGemini 4 ArgonGemini 4 Argon 1,048,576 · Jamba Large 256,000 · o4-mini-deep-research 200,000 tokens
- Widest inputsGemini 4 Argono4-mini-deep-research: Text, Images · Gemini 4 Argon: Text, Images, PDFs, Audio, Video · Jamba Large: Text
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | o4-mini-deep-research | Gemini 4 Argon | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 25 | 20 | 23 |
| Inputs & features | 30% | 60 | 100 | 35 |
| Context window | 20% | 32 | 61 | 36 |
| Overall | 100% | 37/100 | 52/100 | 29/100 |
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 | o4-mini-deep-researchOpenAI | Gemini 4 ArgonGoogle | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 166.1 | — |
| ECI rank | — | #7 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 96.4% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 86.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 100% | — |
| SimpleQA VerifiedShort factual questions | — | 76.0% | — |
| Price per million tokens | |||
| Input | $1.80 (best) | $2.00 | $2.00 |
| Output | $7.20 (best) | $10.00 | $8.00 |
| Cached input | — | $0.10 | — |
| Blended (3:1) | $3.15 (best) | $4.00 | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Google API | Official AI21 Labs API |
| Limits | |||
| Context window | 200,000 tokens | 1,048,576 tokens (best) | 256,000 tokens |
| Max output | 100,000 tokens (best) | 65,536 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yeslow · medium · high | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | — | gemini-4-argon | jamba-large |
| API providers | 1 | 24 (best) | 1 |
| Released | Jun 26, 2025 | Oct 13, 2026 | Jul 3, 2025 |
| Knowledge cutoff | May 2024 | Mar 2026 | Aug 22, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o4-mini-deep-research$32.40
- Gemini 4 Argon$40.00
- Jamba Large$36.00
Which should you choose?
Which is better: o4-mini-deep-research, Gemini 4 Argon or Jamba Large?
Gemini 4 Argon is the better all-round choice, scoring 52/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on inputs & features and context window. o4-mini-deep-research wins on price. 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, o4-mini-deep-research, Gemini 4 Argon or Jamba Large?
o4-mini-deep-research is cheaper at $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); Gemini 4 Argon costs $2.00 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $3.15 per million tokens for o4-mini-deep-research versus $3.50 for Jamba Large (1.1× as much) and $4.00 for Gemini 4 Argon (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o4-mini-deep-research has not been scored yet, Gemini 4 Argon has an ECI of 166.1 and Jamba Large has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o4-mini-deep-research, Gemini 4 Argon and Jamba Large 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?
Gemini 4 Argon has the largest context window at 1,048,576 tokens, against 256,000 for Jamba Large and 200,000 for o4-mini-deep-research. Maximum output per response: o4-mini-deep-research up to 100,000, Gemini 4 Argon up to 65,536, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
o4-mini-deep-research accepts text and images; Gemini 4 Argon accepts text, images, PDFs, audio and video; Jamba Large accepts text. Gemini 4 Argon handles the widest range of inputs.
Are any of these open source?
Jamba Large publishes its weights and can be self-hosted; o4-mini-deep-research and Gemini 4 Argon is proprietary.
Which is newer?
Gemini 4 Argon is the newest, released Oct 13, 2026. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: o4-mini-deep-research May 2024, Gemini 4 Argon Mar 2026, Jamba Large Aug 22, 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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