Verdict: For most people doing real work — building, automating, shipping — Claude Opus 5 is the stronger everyday pick: it self-checks, it wins on the hardest novel-reasoning benchmark by a wide margin, and it costs $5 less per million output tokens than GPT‑5.6 Sol. GPT‑5.6 Sol is the better tool for long, high-throughput coding and agentic builds where raw execution speed, parallel sub-agents, and programmatic tool calling matter more than careful reasoning. The honest answer is that they were never measured against each other on the same day — the scorecards compare different fights — so the right choice depends on the job in front of you, not the leaderboard.
Last verified: 2026-07-31 · Best for developers: GPT‑5.6 Sol (coding speed +
ultraparallel agents) · Best for reasoning + self-checking: Claude Opus 5 (ARC‑AGI‑3, Frontier‑Bench) · Cheaper per output token: Claude Opus 5 ($25 vs $30 per 1M) · Pricing/limits/versions change often — last checked July 31, 2026.
What are GPT‑5.6 Sol and Claude Opus 5?
GPT‑5.6 Sol is OpenAI's flagship model, released July 9, 2026, and the top tier of a three-model family (Sol, Terra, Luna). Claude Opus 5 is Anthropic's newest Opus-generation model, released July 24, 2026. The two launched 15 days apart, and each vendor published benchmark charts that compared its new model against the other company's older model — not the one shipping at the same time. That gap is the reason side-by-side scorecards are misleading, and the reason this comparison exists.
GPT‑5.6 Sol vs Claude Opus 5: the specs at a glance
| Spec | GPT‑5.6 Sol | Claude Opus 5 |
|---|---|---|
| Released | July 9, 2026 (OpenAI) | July 24, 2026 (Anthropic) |
| API model ID | gpt-5.6-sol |
claude-opus-5 |
| Input price | $5.00 / 1M tokens | $5.00 / 1M tokens |
| Output price | $30.00 / 1M tokens | $25.00 / 1M tokens |
| Context window | ~1.05M tokens | 1M tokens |
| Max output | 128K tokens | 128K tokens |
| Reasoning control | Effort ladder up to ultra (4 parallel agents) |
Effort ladder: low → high → xhigh → max |
| Thinking default | Configurable | On by default |
| Flagship agent feature | Programmatic tool calling | Self-verification + sub-agent coordination |
Sources: OpenAI GPT‑5.6 announcement, Anthropic Opus 5 announcement, Claude Platform release notes. Pricing confirmed on both vendors' pages as of July 31, 2026. Note: OpenAI cut Luna's price 80% and Terra's 20% on July 30, 2026 — Sol pricing was unchanged.
How do their benchmark scores actually compare?
Here is where the "different fights" problem becomes concrete. Below is what each vendor reported at launch, with the comparison model each was measured against in parentheses — and where available, an independent cross-vendor number.
| Benchmark | GPT‑5.6 Sol | Claude Opus 5 | Who leads |
|---|---|---|---|
| Artificial Analysis Coding Agent Index | 80 (vs Fable 5 at 77.2 — state of the art at launch) | Not yet independently scored on this index | Sol (OpenAI, Artificial Analysis) |
| Terminal‑Bench 2.1 | 88.8% (91.9% with ultra) |
Not reported on this exact eval | Sol (OpenAI) |
| Frontier‑Bench v0.1 (agentic coding) | 34.4% | 43.3% — more than double Opus 4.8's 18.7% | Opus 5 (Anthropic; Sol figure via codersera analysis) |
| ARC‑AGI‑3 (novel reasoning) | 7.8% | 30.2% — roughly 3× the next-best model, ~4× Sol | Opus 5 (Anthropic; comparison via Vellum) |
| BrowseComp (web research) | 90.4% (92.2% with ultra) |
Not directly reported on this eval | Sol (OpenAI) |
| Zapier AutomationBench (end-to-end business tasks) | Not reported | ~1.5× the next-best model at the same cost; hit 100% on a churn-prevention flow | Opus 5 (Anthropic; confirmed by Zapier CEO Wade Foster) |
| OSWorld 2.0 (computer use) | Strong (per OpenAI charts) | Beats Fable 5's best result at ~1/3 the cost | Opus 5 (Anthropic) |
| GDPval‑AA v2 (knowledge work, Elo) | 1,736 | 1,861 | Opus 5 (Anthropic; Sol figure via codersera) |
The pattern: Sol leads on benchmarks that reward speed, throughput, and command-line/terminal execution. Opus 5 leads on benchmarks that reward novel reasoning, careful self-checking, and end-to-end business automation. An independent cross-vendor comparison on BenchLM (verified July 28, 2026) scored Opus 5 at 82.81 versus Sol at 81.39 across 26 shared benchmarks — close, with Opus 5 ahead overall, but the per-category split tells the same story: Sol wins agentic/terminal tasks, Opus 5 wins coding-quality and reasoning.
What makes GPT‑5.6 Sol different?
Three things set Sol apart from every model Anthropic ships.
1. Programmatic tool calling. Instead of firing every single tool result back through the model, Sol can write and run small JavaScript programs in an isolated runtime that coordinate multiple tools in one shot — calling tools in parallel, looping over results, and only returning the distilled answer to the model. This means fewer API round-trips and less waiting on tool-heavy agent jobs. (OpenAI developer docs)
2. Ultra mode runs four parallel agents by default. When you set effort to ultra, Sol decomposes a big task and assigns it to four sub-agents that work in parallel and synthesize results. OpenAI's own charts show this shifts the cost-latency frontier up: Terminal‑Bench jumps from 88.8% to 91.9%, and BrowseComp from 90.4% to 92.2%. In the API, developers can replicate this with the multi-agent beta in the Responses API. (OpenAI GPT‑5.6 announcement)
3. A three-tier family for price control. Sol is the flagship ($5/$30), but Terra ($2.50/$15) and Luna ($1/$6) let you drop to a cheaper tier for workloads that do not need frontier quality — something Anthropic's Opus line does not offer at the same granularity. (OpenAI pricing table)
What makes Claude Opus 5 different?
Three things set Opus 5 apart, and the first is the one most people miss.
1. It checks its own work without being told to. Anthropic's headline behavioral claim is that Opus 5 verifies its output by default — it goes back and looks at what it produced, spots issues, and fixes them before handing work back. The concrete example they cite: an early-access team gave Opus 5 a build, and it opened its own output in a browser at desktop and phone size, spotted a button that had drifted off-screen, and fixed it before returning. That self-checking habit is what you feel when you are building something real. (Anthropic Opus 5 announcement)
2. It wins on the hardest novel-reasoning benchmark by a wide margin. On ARC‑AGI‑3 — which drops a model into unfamiliar, turn-based environments with no instructions and no stated goals — Opus 5 scored 30.2%, roughly three times the next-best model and about four times Sol's 7.8%. Humans still clear 100% of the environments, so the test is not saturated, but the jump from under 8% to over 30% in one release is the single biggest generational delta in the comparison. (Anthropic; Vellum analysis)
3. It coordinates sub-agents with a writer/verifier pattern. Anthropic says Opus 5 coordinates sub-agents well using a writer-and-verifier split, and that it rarely has agents overriding each other's work. If you have ever had two agents fight over the same file, you know why that matters. Its vision also got sharper — it reads charts, documents, and diagrams, and can rebuild an interface from a picture. (Anthropic Opus 5 announcement)
Which model should you pick for your job?
The scoreboard will not decide this for you — the harness and the job will. Here is a practical split.
Pick GPT‑5.6 Sol when:
- You are running long, high-throughput coding jobs where execution speed and terminal competence matter most (it tops the Artificial Analysis Coding Agent Index at 80 and Terminal‑Bench at 88.8%).
- You want parallel sub-agents out of the box (
ultramode, four agents by default) — this maps well to research sweeps and ambiguous specs where exploration beats serial depth. - Your workload is tool-heavy and benefits from programmatic tool calling (fewer round-trips, faster completion).
- You want price-tier flexibility (drop to Terra or Luna for cheaper loops without switching model families).
Pick Claude Opus 5 when:
- You are doing work where self-checking matters — landing pages, layouts, anything that should be reviewed at desktop and phone size before delivery. Opus 5 does this on its own; Sol does not.
- The job rewards novel reasoning over pattern-matching (ARC‑AGI‑3, open-ended problem-solving, unfamiliar codebases).
- You want end-to-end business automation (Zapier AutomationBench: ~1.5× the next-best model, 100% on a full churn-prevention flow).
- Output cost is a factor — at $25 vs $30 per 1M output tokens, Opus 5 is ~17% cheaper on output, and on long agentic jobs output dominates the bill.
Run both, in separate lanes: The strongest pattern is not picking one — it is running both and matching each to the job. A setup where Sol handles the long autonomous builds and Opus 5 handles planning and the review pass beats either model on its own. If you go this route, the two pieces that make it work are: (1) a shared memory file both models can read so they act as one system, and (2) never running two models in the same project folder at the same time. For a deeper look at how to structure that kind of multi-model agent harness, see How to Build a Skill-Centric AI Agent Harness: The New Feature Pipeline.
What is the one setting almost everyone gets wrong on Opus 5?
Do not tell Claude Opus 5 to add a verification step. Older prompts from earlier models often say things like "Verify your work at the end" or "Use a sub-agent to check." Anthropic says Opus 5 already self-verifies by default, and those old instructions make it over-verify — it spends tokens and time re-checking work it already checked, which slows you down without improving quality. Strip those instructions out of your prompts when you migrate to Opus 5. (Anthropic Opus 5 announcement)
How do you use the effort dial on each model?
Both models have an effort dial, and using it well is where most of the cost savings come from.
On Claude Opus 5: start at high. Go down when quality holds to save tokens and time. Go up to max only when the job really deserves it. The ladder runs low → medium → high → xhigh → max. (Claude Platform release notes)
On GPT‑5.6 Sol: the ladder runs standard → max → ultra. max gives a single agent more time to reason and check its own approach. ultra goes further and runs four agents in parallel — higher token use, but stronger results and faster time-to-result on demanding, parallelizable tasks. (OpenAI GPT‑5.6 announcement)
There is also a fast mode on Opus 5 that runs at roughly 2.5× the normal speed at 2× the base price. Use it when you are iterating on a build and the waiting is what is slowing you down. (Claude Platform release notes)
What does this mean for you?
If you are a developer or a small team building with AI agents, the practical takeaway is this: the model is not the moat — the harness is. Where you run the model, how easily you can keep multiple jobs going, whether your memory system is in one place both models can read, and whether you keep each model in its own lane — those decisions will decide your output quality more than the 1.4-point gap on any leaderboard. Both of these models are good enough that the bottleneck has moved from "which model is smarter" to "are you set up to use either of them well." If you are starting from scratch, prioritizing the harness over the model is the single highest-leverage decision you can make. For a concrete setup guide on running parallel agents, see How to Run Parallel AI Coding Agents for Free in 2026 (Orca Setup Guide).
If your work is more about using AI as a coworker than building agent systems, the same logic applies in a simpler form: pick Opus 5 for the careful, self-checking jobs (planning, review, layouts, research), and pick Sol for the fast, tool-heavy, long-running jobs (coding sprints, data pipelines, agentic automation). For a side-by-side of how Claude and ChatGPT work as everyday AI coworkers, see How to Use Claude Cowork and ChatGPT Work as AI Coworkers (2026).
How do these models fit into the broader frontier landscape?
Sol and Opus 5 are the two newest frontier models, but they are not the only options worth knowing. Open-weight models like Kimi K3 from Moonshot are pushing the price floor down, and Ling 3.0 Flash offers a free 124B-parameter mixture-of-experts model for agent work. The frontier is bifurcating: proprietary models (Sol, Opus 5, Claude Fable 5, Gemini 3.x) compete on capability per dollar, while open-weight models compete on cost and self-hosting control. Where you land depends on whether your constraint is quality ceiling, total cost, or data residency — not on which vendor won the last benchmark cycle.
FAQ
Q: Is GPT‑5.6 Sol or Claude Opus 5 smarter overall? A: They are close, and it depends on the task. An independent cross-vendor comparison (BenchLM, July 28, 2026) scored Opus 5 at 82.81 and Sol at 81.39 across 26 shared benchmarks — a 1.4-point edge to Opus 5. But Sol leads on agentic/terminal tasks and Opus 5 leads on novel reasoning and self-checking, so "smarter" depends on the job.
Q: Which model is cheaper? A: Claude Opus 5 is cheaper on output: $25 vs $30 per 1M output tokens (a ~17% saving). Input price is identical at $5 per 1M tokens. Because long agent runs are output-heavy, the output-price difference is the one that matters in practice.
Q: Does GPT‑5.6 Sol really run four agents in parallel?
A: Yes. When you set effort to ultra, Sol coordinates four sub-agents in parallel by default and synthesizes their results. OpenAI's charts show this lifts Terminal‑Bench from 88.8% to 91.9% and BrowseComp from 90.4% to 92.2%. In the API, developers can replicate this with the multi-agent beta in the Responses API.
Q: Does Claude Opus 5 really check its own work? A: Yes — Anthropic's headline claim is that Opus 5 self-verifies without being instructed to. One cited example: it opened its own build in a browser at desktop and phone size, spotted a button that had drifted off-screen, and fixed it before returning the work. The practical effect is that you should strip out old "verify your work" prompts, which cause Opus 5 to over-verify.
Q: Should I run both models? A: If your work spans build-heavy and review-heavy tasks, yes. The strongest pattern is to let Sol run long autonomous builds and let Opus 5 handle planning and the review pass. Keep a shared memory file both models can read, and never run two models in the same project folder at the same time — they will overwrite each other's work.
Q: What is the context window on each model? A: Both support roughly 1 million tokens of context — Sol at ~1.05M, Opus 5 at 1M. Both cap max output at 128K tokens. Neither is the limiting factor for most agent workflows; the limiting factor is how you route work between them.
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