GPT-5.6 Sol vs Gemini 3.1 Pro: I Ran the Numbers Both Labs Publish
OpenAI's July flagship against Google's February flagship — still the two models everyone's actually switching between in August. Here's where each one wins, with the benchmark receipts.
- Gemini 3.1 Pro edges GPQA Diamond at 94.3% vs GPT-5.6 Sol's 91.4% — Google still owns raw graduate-level science reasoning.
- GPT-5.6 Sol wins agentic ground: 88.8% on Terminal-Bench 2.1 and a 128K-token output ceiling, double Gemini's 65,536.
- Both sit at roughly the same context window — 1,048,576 for Gemini, 1,050,000 for Sol — so 'who has more context' is a wash this round.
- Sol costs more per million: $5 in / $30 out when this ran (OpenAI cut it to $4 / $20 on August 21), vs Gemini's $2.00 in / $12.00 out.
GPT-5.6 Sol (OpenAI, released July 9, 2026) and Gemini 3.1 Pro (Google, released February 19, 2026) are the two frontier models most builders compare as of mid-August 2026. Gemini leads pure reasoning (94.3% GPQA Diamond vs Sol's 91.4%); Sol leads agentic coding (88.8% Terminal-Bench 2.1) and output length (128K vs 65,536 tokens). Pricing and context windows are close to parity.
Every few weeks somebody in my group chat asks which model to default their agent to, and every few weeks the honest answer is “it depends, and here’s why.” August 2026 is no different. The two models people are actually A/B-testing right now are OpenAI’s GPT-5.6 Sol, out July 9th, and Google’s Gemini 3.1 Pro, out back in February but still the reasoning benchmark to beat. Neither is new. Both are current. That’s the frontier now — it doesn’t reset every month, it just accumulates challengers.
I pulled the numbers straight from each lab’s own benchmark disclosures and OpenRouter’s live pricing feed, not vibes.
Where Gemini wins
Gemini 3.1 Pro posts 94.3% on GPQA Diamond, a graduate-level science reasoning test built specifically to resist memorization. That’s the highest score either model has published on that benchmark, and it’s not close — Sol lands at 91.4%. If your workload is research synthesis, scientific literature, or anything where “reasoned through it correctly” beats “moved fast,” Gemini is still the one to beat.
Where Sol wins
Flip to agentic work and the lead flips too. GPT-5.6 Sol scores 88.8% on Terminal-Bench 2.1, the benchmark that measures whether a model can actually complete multi-step command-line tasks without a human steering — the thing your coding agent is doing all day. On TAU-Bench, a tool-use benchmark, Sol edges Gemini 77.3% to 74.7% on comparable infrastructure. And Sol’s output ceiling is double Gemini’s: 128,000 tokens in a single response versus 65,536. That matters when an agent needs to write a long diff or a full report in one pass instead of chunking it.
The receipts
| Metric | GPT-5.6 Sol (OpenAI) | Gemini 3.1 Pro (Google) |
|---|---|---|
| Release date | July 9, 2026 | February 19, 2026 |
| GPQA Diamond | 91.4% | 94.3% |
| TAU-Bench (tool use) | 77.3% | 74.7% |
| Terminal-Bench 2.1 (agentic coding) | 88.8% | not published |
| Context window | 1,050,000 tokens | 1,048,576 tokens |
| Max output | 128,000 tokens | 65,536 tokens |
| Price (per 1M tokens, in / out) | $5 / $30 at publication ($4 / $20 since Aug 21) | $2.00 / $12.00 |
Sources: OpenRouter — GPT-5.6 Sol, OpenRouter — Gemini 3.1 Pro Preview, OpenAI: Sol price reduction, CloudZero: GPT-5.6 price history.
Where I land
Nobody should be running one model for everything in 2026 — that ship sailed the same week the export-ban blackout proved a single-model dependency is an outage waiting to happen. Gemini 3.1 Pro is my pick when the job is reasoning-heavy and I need the answer right the first time: research, science, anything graduate-level. GPT-5.6 Sol is my pick when I’m handing an agent a terminal and walking away — the Terminal-Bench number and the doubled output ceiling both point at “let it run longer, unsupervised.”
Price gap is real. Sol cost 2.5x Gemini per token, in and out, when this ran, and OpenAI cut it to $4 / $20 on August 21 still leaves it at 2x on input and about 1.7x on output. That’s enough to send volume work to Gemini by default. Route by task, same logic I use picking between GPT-5.6 vs Claude Opus 4.8 or deciding ChatGPT vs Gemini for a given build. The models that win are the ones you stop asking to be generalists.
This read on OpenAI is Micah Berkley’s, published under Run the Eval’s no-hype standard. #TheAIMogul
Correction, September 22, 2026: An earlier version of this article listed GPT-5.6 Sol at $2.50 / $15 per million tokens and called it about 25% more expensive than Gemini 3.1 Pro. $2.50 / $15 was the launch price of GPT-5.6 Terra. Sol cost $5 / $30 when this piece published, and OpenAI cut it to $4 / $20 on August 21. The price comparisons above have been corrected.
Bottom lineGemini 3.1 Pro for research, science, and long-form reasoning where accuracy per token matters more than agent autonomy. GPT-5.6 Sol for coding agents, terminal work, and anything that needs a long output, not just a long input. Run both, and route volume work to Gemini: Sol costs 2.5x as much per token at publication, and still about 2x after OpenAI's August 21 price cut.