Compare / head-to-head
GPT-6 Solvs
Jamba Large 1.7
GPT-6 Sol and Jamba Large 1.7 do not yet share a public benchmark, so quality cannot be compared apples-to-apples. Jamba Large 1.7 is 1.2x cheaper per token. GPT-6 Sol has the larger context window (1.1M tokens).
Benchmarks from cited public sources; pricing from official pages; status from official provider feeds.
Shared benchmarks
0
no overlap yet
Cheaper per token
Jamba Large 1.7
1.2x cheaper, input + output
Larger context
GPT-6 Sol
1.1M tokens
Providers
2 providers
OpenAI · AI21 Labs
GPT-6 Sol
OpenAI · released 2026-09-22
textvision
Jamba Large 1.7
AI21 Labs · released 2025-07-03
text
Quality
Benchmark matrix
0 shared · 27 only GPT-6 Sol · 2 only Jamba Large 1.7| Benchmark | GPT-6 Sol | Jamba Large 1.7 | Δ | Edge |
|---|---|---|---|---|
| Reported by both · 0 | ||||
| No public benchmark is reported for both models yet. | ||||
| Only GPT-6 Sol reports · 27 | ||||
| GPQA Diamond | 94.3% ↗epoch run | not reported | — | — |
| LMArena Elo | 1457.2 ↗ | not reported | — | — |
| Agents' Last Exam V1 | 56.4% ↗ | not reported | — | — |
| APEX-Agents (Mercor) | 54.3% ↗ | not reported | — | — |
| AutomationBench 1.0.6 | 33.2% ↗ | not reported | — | — |
| DeepSWE v1.1 | 68.8% ↗ | not reported | — | — |
| EBR-bench (Epoch AI run) | 53.3% ↗epoch run | not reported | — | — |
| FrontierMath Tier 4 v2 (Epoch AI run) | 90% ↗epoch run | not reported | — | — |
| FrontierMath Tiers 1-3 v2 (Epoch AI run) | 89.8% ↗epoch run | not reported | — | — |
| Furniture Assembly (Epoch AI run) | 58.3% ↗epoch run | not reported | — | — |
| LiveBench Agentic Coding (LiveBench) | 52.9% ↗ | not reported | — | — |
| LiveBench Coding (LiveBench) | 81.8% ↗ | not reported | — | — |
| LiveBench Data Analysis (LiveBench) | 81.2% ↗ | not reported | — | — |
| LiveBench Instruction Following (LiveBench) | 68.6% ↗ | not reported | — | — |
| LiveBench Language (LiveBench) | 85.3% ↗ | not reported | — | — |
| LiveBench Mathematics (LiveBench) | 96.4% ↗ | not reported | — | — |
| LiveBench Reasoning (LiveBench) | 88.7% ↗ | not reported | — | — |
| LMArena Agent (LMArena) | 0.0971 ↗ | not reported | — | — |
| LMArena WebDev (LMArena) | 1689 ↗ | not reported | — | — |
| Mystery Game Puzzles (Epoch AI run) | 56% ↗epoch run | not reported | — | — |
| OSWorld 2.0 (offline, partial) | 60.5% ↗ | not reported | — | — |
| OTIS Mock AIME 2024-2025 (Epoch AI run) | 100% ↗epoch run | not reported | — | — |
| SAGE (Vals AI) | 44.8% ↗ | not reported | — | — |
| SimpleBench (SimpleBench) | 73.1% ↗ | not reported | — | — |
| SimpleQA Verified | 60.7% ↗epoch run | not reported | — | — |
| Terminal-Bench 4.0 (Vals AI) | 44.4% ↗ | not reported | — | — |
| Vending-Bench 2 (Andon Labs) | 14427.85 ↗ | not reported | — | — |
| Only Jamba Large 1.7 reports · 2 | ||||
| FACTS | not reported | 83.2% ↗ | — | — |
| IFEval | not reported | 84% ↗ | — | — |
Scores tagged "epoch" or "matharena" are independent runs, used only where the lab has not published its own; ⚠ marks rows MathArena flags as released after the competition. Higher is better on every row. Δ is GPT-6 Sol minus Jamba Large 1.7 in the benchmark's own unit. "Not reported" means the lab has not published that figure; it is not a zero. ↗ opens the source.
Specs & pricing
Side by side
Official pricing pages and model cards| Spec | GPT-6 Sol | Jamba Large 1.7 | Edge |
|---|---|---|---|
| Context window | 1.1M tokens | 256K tokens | GPT-6 Sol |
| Max output | 128K tokens | 4K tokens | GPT-6 Sol |
| Input price / 1M | $2 ↗ | $2 ↗ | Tie |
| Output price / 1M | $10 ↗ | $8 ↗ | Jamba Large 1.7 |
| Cached input / 1M | $0.2 ↗ | — | — |
| Input + output / 1M Lower is cheaper. List prices; batch, tool and regional fees excluded. | $12 | $10 | Jamba Large 1.7 |
| Modalities | text · vision | text | GPT-6 Sol |
| Released | 2026-09-22 | 2025-07-03 | — |
| Cited benchmark scores | 27 | 2 | — |
Reliability
Provider status
Live from /statusMore matchups
GPT-6 Sol vs …
Models sharing the most benchmarksMore matchups
Jamba Large 1.7 vs …
Models sharing the most benchmarksBuilt by Respan
Which one wins on your data?
Public benchmarks are a starting point. Run GPT-6 Sol and Jamba Large 1.7 on your own prompts with Respan evals, or route to either through one gateway key with automatic failover.