Jamba Large vs o4-mini-deep-research vs Claude Sonnet 5.5
Claude Sonnet 5.5 comes out ahead, 46 to 37 and 29 on our weighted score, though o4-mini-deep-research is 21% cheaper per token.
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
- Our pick
AnthropicClaude Sonnet 5.5
46/100- ECI166.7
- Price$2.00 / $10.00
- Context1M
Claude Sonnet 5.5 is our pick
Claude Sonnet 5.5 is the better all-round choice, scoring 46/100 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 · Claude Sonnet 5.5 $4.00 per 1M tokens (3:1 blend)
- Longest contextClaude Sonnet 5.5Claude Sonnet 5.5 1,000,000 · Jamba Large 256,000 · o4-mini-deep-research 200,000 tokens
- Widest inputsClaude Sonnet 5.5Jamba Large: Text · o4-mini-deep-research: Text, Images · Claude Sonnet 5.5: Text, Images, PDFs
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | Jamba Large | o4-mini-deep-research | Claude Sonnet 5.5 |
|---|---|---|---|---|
| Price | 50% | 23 | 25 | 20 |
| Inputs & features | 30% | 35 | 60 | 80 |
| Context window | 20% | 36 | 32 | 60 |
| Overall | 100% | 29/100 | 37/100 | 46/100 |
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 | Jamba LargeAI21 Labs | o4-mini-deep-researchOpenAI | Claude Sonnet 5.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 166.7 |
| ECI rank | — | — | #5 of 165 |
| GPQA DiamondGraduate-level science questions | — | — | 95.6% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 88.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 100% |
| SimpleQA VerifiedShort factual questions | — | — | 46.5% |
| Price per million tokens | |||
| Input | $2.00 | $1.80 (best) | $2.00 |
| Output | $8.00 | $7.20 (best) | $10.00 |
| Cached input | — | — | $0.10 |
| Blended (3:1) | $3.50 | $3.15 (best) | $4.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official AI21 Labs API | Median of 1 providers | Official Anthropic API |
| Limits | |||
| Context window | 256,000 tokens | 200,000 tokens | 1,000,000 tokens (best) |
| Max output | 4,096 tokens | 100,000 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | jamba-large | — | claude-sonnet-5-5 |
| API providers | 1 | 1 | 34 (best) |
| Released | Jul 3, 2025 | Jun 26, 2025 | Sep 28, 2026 |
| Knowledge cutoff | Aug 22, 2024 | May 2024 | Jun 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Jamba Large$36.00
- o4-mini-deep-research$32.40
- Claude Sonnet 5.5$40.00
Which should you choose?
Which is better: Jamba Large, o4-mini-deep-research or Claude Sonnet 5.5?
Claude Sonnet 5.5 is the better all-round choice, scoring 46/100 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, Jamba Large, o4-mini-deep-research or Claude Sonnet 5.5?
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); Claude Sonnet 5.5 costs $2.00 input / $10.00 output per million tokens (official Anthropic 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 Claude Sonnet 5.5 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Jamba Large has not been scored yet, o4-mini-deep-research has not been scored yet and Claude Sonnet 5.5 has an ECI of 166.7.
Which is better for coding?
There are no published SWE-bench Verified results for Jamba Large, o4-mini-deep-research and Claude Sonnet 5.5 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?
Claude Sonnet 5.5 has the largest context window at 1,000,000 tokens, against 256,000 for Jamba Large and 200,000 for o4-mini-deep-research. Maximum output per response: Jamba Large up to 4,096, o4-mini-deep-research up to 100,000, Claude Sonnet 5.5 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Jamba Large accepts text; o4-mini-deep-research accepts text and images; Claude Sonnet 5.5 accepts text, images and PDFs. Claude Sonnet 5.5 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 Claude Sonnet 5.5 is proprietary.
Which is newer?
Claude Sonnet 5.5 is the newest, released Sep 28, 2026. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: Jamba Large Aug 22, 2024, o4-mini-deep-research May 2024, Claude Sonnet 5.5 Jun 2026.
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.
