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ModelTape / Models / GPT-6.1 Sol
OpenAI · API-only (closed weights)

GPT-6.1 Sol

GPT-6.1 Sol is an API-only (closed weights) language model from OpenAI, released September 29, 2026. The context window is 1.1M tokens. On the Epoch Capabilities Index it scores 168.5, rank 5 of 256 rated models. API price: $2 input / $10 output per million tokens.

We’re introducing GPT‑6.1 Sol, an upgrade to GPT‑6 Sol that nearly matches GPT‑6 Astra’s intelligence on agentic coding, computer use, and professional work at one-fifth of Astra’s standard input and output token prices.

Check it against your own PC →

Lab
OpenAI
Released
September 29, 2026
Weights
API-only (closed weights)
Parameters
–
Context
1.1M
Price / M tokens
$2 / $10 in / out
Epoch ECI
168.5 #5

Strengths by category

ModelTape's 0–100 fit on Epoch AI benchmark results.

CategoryScoreBased on
Reasoning51.6Chess Puzzles, SimpleBench, Mystery Game Puzzles
Math81.4ProofBench, OTIS Mock AIME 2024-2025, FrontierMath-Tiers-1-3-v2-Private, FrontierMath-Tier-4-v2-Private
Knowledge83.9GPQA diamond, SimpleQA Verified

Benchmark results

Benchmark (Epoch AI)Score
OTIS Mock AIME100.0%
FrontierMath-Tier-4-v2-Private100.0%
ProofBench99.0%
FrontierMath (Tiers 1–3)98.6%
GPQA Diamond97.8%
SimpleBench79.5%
Mystery Game Puzzles78.0%
SimpleQA Verified73.9%
Furniture Assembly71.4%
Chess Puzzles66.3%
MirrorCode61.1%
EBR-bench54.3%
SpireClimb23.8%

Links

Announcement · Epoch AI benchmarks

Models with a similar score

Gemini 4 Carbon · GPT-6 Astra · Claude Opus 5.5 · GPT-6.1 Terra · Claude Fable 5.1 · Claude Sonnet 5.5 · Gemini 4 Argon · Claude Opus 5

More from OpenAI

GPT-6.1 Terra · GPT-6 Sol · GPT-6 Luna · GPT-6 Astra · GPT-5.6 Cyber · Daybreak Red · Daybreak Blue · GPT-5.6 Sol

Data updated Thu, 12 Nov 2026 09:42 UTC. Benchmarks, Capabilities Index and model announcements: Epoch AI, “Capabilities & benchmarking” and “Data on AI models” (CC BY 4.0); category scores are ModelTape's own fit on Epoch's results. Releases and prices: models.dev (MIT). Sizes and quantizations: Hugging Face. “Runs on” figures are estimates from model size, quantization and memory bandwidth (±30–50%).
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