Model comparison
GPT-6.1 Sol vs Jamba Large
GPT-6.1 Sol has enough public results to be ranked (#6); 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-6.1 Sol leads 81.9 to 18.0.
- Jamba Large is cheaper at $2 / $8 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 256K.
- Jamba Large has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Jamba Large | |
|---|---|---|
| Provider | OpenAI | AI21 Labs |
| Noometry Index | 65.6 | 33.1 |
| Released | 2026-09-29 | 2025-07-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 256K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $10 | $8 |
| Results tracked | 34 | 2 |
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Category by category
Coding Not comparable
GPT-6.1 Sol: 63.2 (#8), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
| LMArena Coding | 1487 | — |
Agentic & Tool Use Not comparable
GPT-6.1 Sol: 39.6 (#26), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| APEX-Agents | 60% | — |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Jamba Large: 18.0
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| ARC-AGI-2 | 94.2% | — |
| Kagi LLM Benchmark | — | 26.1% |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| LMArena Hard Prompts | 1466 | — |
| Mystery Game Puzzles | 80% | — |
| Epoch Capabilities Index | 166.09 | — |
Math Not comparable
GPT-6.1 Sol: 93.7 (#1), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| LMArena Math | 1464 | — |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Jamba Large: 37.7
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| GPQA Diamond | 95.4% | — |
| SimpleQA Verified | 73.9% | — |
| Vectara Hallucination Rate | — | 9.7% |
| LMArena Expert | 1502 | — |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual Not comparable
GPT-6.1 Sol: 54.3 (#46), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| LMArena Non-English | 1438 | — |
| LMArena Chinese | 1477 | — |
| LMArena Russian | 1455 | — |
Instruction Following Not comparable
GPT-6.1 Sol: 77.0 (#29), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| LMArena Instruction Following | 1468 | — |
Long Context Not comparable
GPT-6.1 Sol: 44.9 (#54), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| LMArena Longer Query | 1465 | — |
Writing & Preference Not comparable
GPT-6.1 Sol: 63.6 (#63), Jamba Large: —
| Benchmark | GPT-6.1 Sol | Jamba Large |
|---|---|---|
| LMArena Text | 1447 | — |
| LMArena Creative Writing | 1432 | — |
| LMArena Multi-Turn | 1449 | — |
Frequently asked questions
Is GPT-6.1 Sol better than Jamba Large?
GPT-6.1 Sol has enough public results to be ranked (#6); Jamba Large does not yet, so treat this comparison as directional.
Which is cheaper, GPT-6.1 Sol or Jamba Large?
Jamba Large is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 256K.
How many benchmarks do GPT-6.1 Sol and Jamba Large share?
0 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Jamba Large has 2.