Model comparison
GPT-5.6 Luna vs Jamba Large
GPT-5.6 Luna has enough public results to be ranked (#24); Jamba Large does not yet, so treat this comparison as directional.
Last verified . 1 shared benchmarks.
Summary
- They share 1 benchmark with published results for both. GPT-5.6 Luna scores higher in 2 categories and Jamba Large in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Luna leads 47.5 to 17.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 49.1% for GPT-5.6 Luna and 26.1% for Jamba Large.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $2 / $8 for Jamba Large.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 256K.
- Jamba Large has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Jamba Large | |
|---|---|---|
| Provider | OpenAI | AI21 Labs |
| Noometry Index | 54.7 | 32.6 |
| Released | 2026-07-09 | 2025-07-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 256K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.20 | $2 |
| Output $ / M tokens | $1.20 | $8 |
| Results tracked | 51 | 2 |
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Category by category
Coding Not comparable
GPT-5.6 Luna: 53.9 (#25), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1517 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| LMArena Coding | 1466 | — |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 35.3 (#37), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.5 (#38), Jamba Large: 17.4
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| Kagi LLM Benchmark | 49.1% | 26.1% |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| CritPt | 20.6% | — |
| Chess Puzzles | 40% | — |
| LMArena Hard Prompts | 1452 | — |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
| Epoch Capabilities Index | 156.32 | — |
Math Not comparable
GPT-5.6 Luna: 77.5 (#12), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 60% | — |
| LMArena Math | 1455 | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.6 (#31), Jamba Large: 37.0
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 9.7% |
| LMArena Expert | 1479 | — |
Multimodal Not comparable
GPT-5.6 Luna: 43.0 (#22), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| LMArena Vision | 1259 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Not comparable
GPT-5.6 Luna: 53.0 (#73), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| LMArena Non-English | 1418 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1457 | — |
| LMArena German | 1459 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1414 | — |
| LMArena Russian | 1428 | — |
| LMArena Spanish | 1445 | — |
Instruction Following Not comparable
GPT-5.6 Luna: 76.0 (#48), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| LMArena Instruction Following | 1440 | — |
Long Context Not comparable
GPT-5.6 Luna: 43.9 (#78), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| LMArena Longer Query | 1437 | — |
Writing & Preference Not comparable
GPT-5.6 Luna: 68.3 (#27), Jamba Large: —
| Benchmark | GPT-5.6 Luna | Jamba Large |
|---|---|---|
| LMArena Text | 1432 | — |
| LMArena Creative Writing | 1397 | — |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
| LMArena Multi-Turn | 1433 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Jamba Large?
GPT-5.6 Luna has enough public results to be ranked (#24); Jamba Large does not yet, so treat this comparison as directional.
Which is cheaper, GPT-5.6 Luna or Jamba Large?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Jamba Large lists at $2 and $8.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 256K.
How many benchmarks do GPT-5.6 Luna and Jamba Large share?
1 benchmark has published results for both models. GPT-5.6 Luna has 51 scored results on Noometry and Jamba Large has 2.