Reviews
GPT-5.6 Sol Review: 7 Reasoning Wins (And 3 Losses)
An honest, benchmark-driven review of OpenAI's GPT-5.6 Sol. It smashes SWE-bench and ARC-AGI-2, but Claude Fable 5 still edges it on GPQA. Worth the hype?
September 15, 2026
OpenAI's GPT-5.6 Sol just posted a 96.2% score on SWE-bench Verified, the highest ever recorded on the benchmark. That single data point is why every serious developer I know is re-evaluating their model stack this quarter.
But a great SWE-bench score doesn't automatically make a great reasoning model. And Sol has some peculiar weaknesses that the marketing decks conveniently skip over.
So let's do the honest version.
Quick Verdict
| Rating | 9.1 / 10 |
| One-line verdict | The strongest reasoning + coding hybrid model shipping in 2026, but not the outright champion on every benchmark. |
| Best for | Senior engineers, agentic coding workflows, and researchers pushing long-horizon reasoning problems. |
| Skip if | You mostly need creative writing, cheap bulk inference, or the absolute top GPQA score. |
What Is GPT-5.6 Sol?
GPT-5.6 Sol is OpenAI's mid-cycle refresh of the GPT-5 series, positioned as the reasoning-optimized sibling to the upcoming GPT-6 Astra. It's the successor to GPT-5.5 and the third model in the "Sol" reasoning subline, positioned as OpenAI's answer to the Claude Opus 4.7/4.8 wave and Gemini 3.1 Pro.
According to OpenAI's model page, Sol runs a mixture-of-experts architecture with an extended chain-of-thought scratchpad. The company hasn't published parameter counts (they haven't done that since GPT-4), but inference latency suggests something significantly leaner than GPT-5 Codex under the hood.
And yes, it's the model that finally broke 90% on ARC-AGI-2.
Key Features Worth Talking About
1. The SWE-bench Verified Number
96.2% on SWE-bench Verified, the industry-standard benchmark for autonomous code repair on real GitHub issues. That's ahead of Claude Opus 5 (96%) and Claude Fable 5 (95.5%). It's not a huge lead. But when you're operating this close to ceiling, every fraction of a point represents a meaningful class of bugs the model can now solve unattended.
2. ARC-AGI-2 Performance
GPT-5.6 Sol hits 92.5% on ARC-AGI-2, per the ARC Prize leaderboard. Only GPT-6 Astra scores higher at 95%, and Astra isn't publicly available yet. This is a big deal because ARC-AGI-2 was specifically designed to be brutal for pattern-completion LLMs. The fact that a shipping model is now clearing 92% suggests the reasoning architecture is actually generalizing, not just memorizing.
3. Extended Thinking Modes
Sol ships with three thinking presets: Fast, Deep, and Sol-Max. Sol-Max lets the model burn up to roughly 400K reasoning tokens on a single problem before producing an answer. That's the setting that produces the headline benchmark numbers, and also the setting that will destroy your API budget if you're not careful.
4. Tool Use and Agentic Loops
The tool-calling reliability is genuinely better than GPT-5.5. Based on the OpenAI cookbook examples, Sol handles nested tool calls with 5+ steps without losing thread context. This matters more than the benchmark scores if you're building agents.
5. Context Window
256K tokens standard, with a 1M-token "long context" variant available on the enterprise tier. That's competitive but not class-leading. Gemini 3.1 Pro still owns the ultra-long-context crown.
6. Multimodal Improvements
Vision reasoning got a real upgrade. Chart interpretation, diagram reading, and screenshot-to-code all work noticeably better than in GPT-5.5. Not gonna lie, this is where the model surprised me most reading through the release notes.
7. Latency Under Load
Time-to-first-token in Fast mode is roughly 40% quicker than GPT-5.5 according to third-party latency trackers like Artificial Analysis. Sol-Max obviously runs slower because it's doing more actual thinking.
Real-World Benchmark Breakdown
Here's how Sol stacks up against the current frontier on the benchmarks that actually matter for reasoning:
| Benchmark | GPT-5.6 Sol | Claude Fable 5 | Claude Opus 4.8 | Gemini 3.1 Pro |
|---|---|---|---|---|
| GPQA Diamond | 94.1% | 94.6% | 93.6% | 94.1% |
| SWE-bench Verified | 96.2% | 95.5% | N/A | N/A |
| ARC-AGI-2 | 92.5% | N/A | N/A | N/A |
A few things jump out.
On GPQA Diamond (graduate-level science reasoning), Sol is tied with Gemini 3.1 Pro at 94.1% and half a point behind Claude Fable 5. So if pure scientific reasoning is your workload, Fable 5 has a slight edge. Real, but slight.
On SWE-bench Verified, Sol wins outright. If your workflow is coding-heavy, this is the model to grab.
On ARC-AGI-2, Sol is the best shipping model, period.
The MATH benchmark is worth calling out too: Papers with Code still shows GPT-5.2 Pro at 99% and GPT-5 Codex at 98.7%. OpenAI hasn't published a Sol number for MATH yet, which is a little suspicious. Read into that what you will.
Pricing Analysis: Is It Worth It?
OpenAI is charging premium prices for Sol. Based on the official pricing page (check the OpenAI API pricing for current numbers), the model sits at the top of the GPT-5 lineup, well above GPT-5.5 and considerably above GPT-4o.
And here's the catch: those prices are for input and output tokens only. Reasoning tokens (the internal chain-of-thought) are billed separately at the output rate, which means a single Sol-Max request can burn through 400K billed tokens even if your final answer is 500 tokens long.
A back-of-envelope calculation: running Sol-Max on a hard reasoning problem can easily cost $8-15 per query. For comparison, GPT-4o costs pennies. This matters if you're building a product where every user query hits the model.
Is it worth it? For high-value work where a correct answer beats ten cheap wrong ones, yes. For chatbot volume, absolutely not. Use Sol as a router destination, not a default.
Sol-Max is the first model I'd genuinely trust to solve a problem I can't solve myself. That's worth $10 a query when the problem is worth $10,000.
Pros and Cons
Pros:
- Best-in-class SWE-bench Verified score at 96.2%
- Only shipping model above 90% on ARC-AGI-2
- Genuinely improved tool-calling reliability for agent workflows
- Fast mode latency is competitive with much smaller models
- Vision reasoning finally feels production-ready
Cons:
- Loses to Claude Fable 5 on GPQA Diamond (94.6% vs 94.1%)
- Reasoning token billing can produce genuinely shocking invoices
- No published MATH benchmark score, which is a curious omission
- Context window (256K standard) is behind Gemini 3.1 Pro
- Sol-Max latency can hit 90+ seconds for complex prompts
Who Should Use GPT-5.6 Sol?
Use it if you're:
- A senior developer running agentic coding workflows in Cursor, Claude Code, or Codex
- Building tools where a single correct answer justifies a $5-15 query cost
- Working on scientific research, mathematical proofs, or hard debugging problems
- Running evaluations where SWE-bench and ARC-AGI-2 performance actually correlate with your use case
Skip it if you're:
- Building consumer chatbots (use GPT-4o or Claude Haiku for volume)
- Doing creative writing (Claude Opus 4.8 still owns Chatbot Arena at 1580 Elo)
- Cost-constrained and running high query volumes
- Purely doing pure GPQA-style scientific reasoning (Claude Fable 5 is fractionally better)
Comparing Against the Anthropic Wall
Anthropic's Claude Opus 4.8 tops the LMSYS Chatbot Arena at 1580 Elo, which means human raters still slightly prefer Claude for open-ended conversation. Claude Fable 5 leads on GPQA. Claude Opus 5 is basically tied with Sol on SWE-bench.
So Sol isn't a clean sweep. It's a category leader in reasoning-heavy coding and abstract problem-solving, and roughly tied everywhere else. That's a great position for OpenAI to be in, but the "clearly the best model overall" narrative some analysts are pushing doesn't hold up under scrutiny.
The honest read: pick your model per workload. Sol for hard coding and reasoning. Claude Fable 5 for research chemistry and biology. Claude Opus 4.8 for conversation. Gemini 3.1 Pro for anything that needs a 1M+ token window.
Final Verdict
GPT-5.6 Sol is the most impressive reasoning-focused model OpenAI has shipped since GPT-5 launched. The SWE-bench and ARC-AGI-2 numbers are legitimately category-defining. The tool-calling upgrades matter more than the marketing acknowledges. And the pricing, while high, is defensible if you use the model where it actually shines.
But it's not the second coming. Claude Fable 5 beats it on GPQA. Claude Opus 4.8 outranks it on human preference. Gemini 3.1 Pro out-contexts it. And the reasoning-token billing model is going to bankrupt some startup that forgets to set spend caps.
Final rating: 9.1/10. The best reasoning model you can call today, with real caveats you should actually respect.
Want the head-to-head numbers? Read our GPT-5.6 Sol vs Claude Fable 5 coding verdict, or dig into how open-source options compare in the DeepSeek V4-Pro review and the Qwen 3.8-Max coding review.
