Model comparison
GPT-5.6 Terra vs Jamba Large
GPT-5.6 Terra has enough public results to be ranked (#15); 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 Terra 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 Terra leads 60.6 to 17.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 51.3% for GPT-5.6 Terra and 26.1% for Jamba Large.
- Jamba Large is cheaper at $2 / $8 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra 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 Terra | Jamba Large | |
|---|---|---|
| Provider | OpenAI | AI21 Labs |
| Noometry Index | 59.1 | 32.6 |
| Released | 2026-07-09 | 2025-07-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 256K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $12 | $8 |
| Results tracked | 52 | 2 |
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Category by category
Coding Not comparable
GPT-5.6 Terra: 57.1 (#16), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| LMArena Coding | 1484 | — |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.6 (#19), Jamba Large: 17.4
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| Kagi LLM Benchmark | 51.3% | 26.1% |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Chess Puzzles | 54% | — |
| LMArena Hard Prompts | 1467 | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.31 | — |
Math Not comparable
GPT-5.6 Terra: 81.5 (#10), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
| ProofBench | 74% | — |
| LMArena Math | 1463 | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), Jamba Large: 37.0
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
| Vectara Hallucination Rate | — | 9.7% |
| LMArena Expert | 1492 | — |
Multimodal Not comparable
GPT-5.6 Terra: 47.5 (#10), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual Not comparable
GPT-5.6 Terra: 54.5 (#42), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| LMArena Non-English | 1439 | — |
| LMArena Chinese | 1514 | — |
| LMArena French | 1476 | — |
| LMArena German | 1462 | — |
| LMArena Japanese | 1446 | — |
| LMArena Korean | 1420 | — |
| LMArena Russian | 1450 | — |
| LMArena Spanish | 1455 | — |
Instruction Following Not comparable
GPT-5.6 Terra: 76.6 (#38), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| LMArena Instruction Following | 1453 | — |
Long Context Not comparable
GPT-5.6 Terra: 44.4 (#64), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| LMArena Longer Query | 1452 | — |
Writing & Preference Not comparable
GPT-5.6 Terra: 70.4 (#21), Jamba Large: —
| Benchmark | GPT-5.6 Terra | Jamba Large |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
| LMArena Multi-Turn | 1451 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Jamba Large?
GPT-5.6 Terra has enough public results to be ranked (#15); Jamba Large does not yet, so treat this comparison as directional.
Which is cheaper, GPT-5.6 Terra or Jamba Large?
Jamba Large is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 256K.
How many benchmarks do GPT-5.6 Terra and Jamba Large share?
1 benchmark has published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Jamba Large has 2.