Model comparison
GPT-5.5 Pro vs Jamba Large
GPT-5.5 Pro has enough public results to be ranked (#8); Jamba Large does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in reasoning, where GPT-5.5 Pro leads 73.3 to 18.0.
- Jamba Large is cheaper at $2 / $8 per million input/output tokens, against $30 / $180 for GPT-5.5 Pro.
- GPT-5.5 Pro accepts more context: 1.05M tokens versus 256K.
- Jamba Large has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 Pro | Jamba Large | |
|---|---|---|
| Provider | OpenAI | AI21 Labs |
| Noometry Index | 64.3 | 33.1 |
| Released | 2026-04-23 | 2025-07-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 256K |
| Max output | 128K | 4K |
| Input $ / M tokens | $30 | $2 |
| Output $ / M tokens | $180 | $8 |
| Results tracked | 14 | 2 |
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Category by category
Reasoning GPT-5.5 Pro leads
GPT-5.5 Pro: 73.3 (#10), Jamba Large: 18.0
| Benchmark | GPT-5.5 Pro | Jamba Large |
|---|---|---|
| ARC-AGI-2 | 84.6% | — |
| SimpleBench | 76.9% | — |
| Kagi LLM Benchmark | — | 26.1% |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30.6% | — |
| Chess Puzzles | 64% | — |
| DTBench | 96% | — |
| LMCA | 53.9% | — |
| Epoch Capabilities Index | 162.07 | — |
Math Not comparable
GPT-5.5 Pro: 84.0 (#10), Jamba Large: —
| Benchmark | GPT-5.5 Pro | Jamba Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 87.7% | — |
| FrontierMath Tier 4 | 78% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| FrontierMath (Feb 2025 set) | 52.4% | — |
| FrontierMath Tier 4 (v1) | 39.6% | — |
Knowledge GPT-5.5 Pro leads
GPT-5.5 Pro: 64.1 (#19), Jamba Large: 37.7
| Benchmark | GPT-5.5 Pro | Jamba Large |
|---|---|---|
| GPQA Diamond | 93.9% | — |
| Vectara Hallucination Rate | — | 9.7% |
Frequently asked questions
Is GPT-5.5 Pro better than Jamba Large?
GPT-5.5 Pro has enough public results to be ranked (#8); Jamba Large does not yet, so treat this comparison as directional.
Which is cheaper, GPT-5.5 Pro or Jamba Large?
Jamba Large is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; GPT-5.5 Pro lists at $30 and $180.
Which has the bigger context window?
GPT-5.5 Pro does, with 1.05M tokens against 256K.
How many benchmarks do GPT-5.5 Pro and Jamba Large share?
0 benchmarks have published results for both models. GPT-5.5 Pro has 14 scored results on Noometry and Jamba Large has 2.