GPT-5.6 Sol vs Claude Opus 4.8: In-Depth Comparison of Benchmarks, Coding, Price, and How to Choose

By July 2026, the two models fighting for the lead in AI coding were both on the table. Anthropic Claude Opus 4.8 (released May 28) and OpenAI GPT-5.6's top-tier "Sol" (general availability July 9). GPT-5.6 comes as a three-model lineup — Luna/Terra/Sol — with Sol as its top tier.

Both are head-to-head models billed as "next-generation agent foundations," but their strengths are strikingly opposite. Sol leads in terminal operation and overall agentic capability; Opus 4.8 leads in production-grade coding and "honesty" — the division of labor is clear. In this article we compare the two in depth, based on both companies' official announcements and independent benchmarks (Vellum, Artificial Analysis, and others), and lay out the practical question: "which one should you actually use, and how?"

1. Positioning and philosophy: where the two models differ

At launch, both were aimed at becoming "the star of agentic workloads," but their pitches diverge sharply.

Claude Opus 4.8 — "the craftsman who finishes the job inside a real codebase"

Anthropic placed Opus 4.8's headline not on "stacking up benchmarks" but on "being more honest." It scored 69.2% on SWE-bench Pro, which measures fixes to real GitHub repositories (up +4.9pt from the previous-generation Opus 4.7's 64.3%), holding the lead in production-grade coding. It also foregrounds reliability and honesty metrics: Anthropic's announcement says Opus 4.8 is around four times less likely than its predecessor to let flaws in code it has written pass unremarked, and Anthropic's Transparency Hub reports that it fails to tell the user about important failures in its work (tests that don't pass, features never built) only 3.7% of the time.

GPT-5.6 Sol — "the all-rounder agent that drives the terminal"

OpenAI rolled out GPT-5.6 as three models (Luna/Terra/Sol) and placed Sol at the top. With 88.8% on TerminalBench 2.1 (autonomous terminal operation), 53.6 on Agents' Last Exam (long-horizon real work across 55 fields), and 80 on the Artificial Analysis Coding Agent Index, it takes the lead in planning, terminal operation, and overall agentic capability. It also improved token efficiency by 54% in coding, and is billed as "the most capable cybersecurity model" (sources: OpenAI's official announcement, CNBC, Vellum).

2. Spec at a glance

* Prices and specs are based on each company's official announcements (Opus 4.8 = May 28, 2026; GPT-5.6 = July 9, 2026). Note that benchmark figures use different measurement conditions, timing, and harnesses across the two companies, so this is not a strict apples-to-apples comparison. The head-to-head figures (SWE-bench Pro, TerminalBench 2.1, Agents' Last Exam, Coding Agent Index, GraphWalks, GPQA Diamond, FrontierMath) come from OpenAI's GPT-5.6 evaluation table, which lists both models (the Coding Agent Index is Artificial Analysis's metric). Some of the Opus 4.8 values there do not match Anthropic's own: TerminalBench 2.1, for example, is 78.9% in OpenAI's table and 74.6% in the table in Anthropic's announcement.

3. Benchmark deep-dive comparison

People tend to say "top models are evenly matched," but by benchmark there are clear directional differences. It's fair to say their strong domains are almost opposite.

3-1. Coding

The key point is that "what each benchmark measures" is different. SWE-bench Pro measures patch generation on real GitHub issues — the ability to fix an existing codebase. TerminalBench 2.1, by contrast, is a set of tasks that drive the terminal autonomously from the command line, gauging the performance of the plan-and-execute loop. Opus 4.8 wins the former, Sol the latter — which maps directly onto a practical division: "Opus if you're handling large PRs in a real repo; Sol if you're building from scratch with a CLI or agent."

3-2. Agents and long-horizon tasks

Source: OpenAI's GPT-5.6 evaluation table (the Opus 4.8 values are from the same table). Sol's Agents' Last Exam 53.6 is from the announcement text; the table on the same page shows 52.7%.

In agentic breadth, Sol is broadly stronger. The gap shows up in areas close to "autonomous execution," such as terminal operation and long-horizon composite workflows. Where Opus 4.8 comes out ahead is accurate fixes to real codebases (SWE-bench Pro); on long-context tracking, the same table puts Sol ahead on GraphWalks 1M (77.1% vs 68.1%). It's a "Sol for breadth, Opus for real code fixes" picture.

3-3. Reasoning, math, and reliability

In OpenAI's table, which lists both models, Sol leads on GPQA Diamond (94.6% vs Opus 4.8's 92%), FrontierMath (Tier 1-3: 89% vs 80%; Tier 4: 83% vs 56.1%), and GraphWalks 1M (77.1% vs 68.1%), so math and long context cannot be called Opus's home turf. Anthropic, for its part, foregrounds reliability and honesty: its announcement says Opus 4.8 is around four times less likely than its predecessor to let flaws in code it has written pass unremarked, and its Transparency Hub puts the rate at which it fails to tell the user about important failures at 3.7% (versus 27.6% for Mythos Preview, a five-fold improvement). That pays off in work where the cost of an error is high, such as healthcare, legal, and finance. There are no figures, however, that compare Sol on the same terms.

4. Checking the "undisclosed benchmark" problem — what the table includes and what it leaves out

One thing to watch in this comparison: right after launch, independent analysis (Vellum) noted that OpenAI had not published SWE-bench Verified, GPQA Diamond, AIME, MMLU, ARC-AGI-2, or FrontierMath for GPT-5.6. However, the evaluation table on OpenAI's announcement page, checked on September 22, 2026, does include GPQA Diamond (Sol 94.6%, Opus 4.8 92%) and FrontierMath (Tier 1-3: Sol 89%, Opus 4.8 80%; Tier 4: Sol 83%, Opus 4.8 56.1%), and Sol leads on both. What is missing is SWE-bench Verified, AIME, and MMLU, and Sol's ARC-AGI-2 score (92.5%) first appeared in the table for GPT-6 Astra on September 3.

Even in the evaluation table OpenAI published with GPT-5.6, Sol's SWE-bench Pro is 64.6%, below Opus 4.8's 69.2% (both values from the same OpenAI table). In other words, on the coding metric closest to real work — fixing bugs in real repositories — Opus 4.8 came out ahead. At the same time, OpenAI published an analysis estimating that about 30% of SWE-bench Pro tasks are broken (as Simon Willison noted). Behind the flashy agent-style scores, Claude kept its edge in coding's heartland — and this is the single biggest point that separates the two.

5. Real cost — unit price and token efficiency

On unit price, Opus 4.8 is $25/MTok on output and Sol is $30/MTok — nominally Opus is a little under 20% cheaper. Input is $5 for both (though Sol is on promotional pricing of $4 input / $20 output for three months from August 21, 2026, so during that period Sol is cheaper on input too). But the actual bill changes with "how many tokens you output per task."

• Sol's catch-up factor: OpenAI says coding token efficiency improved by 54%, so if output volume drops, the unit-price gap ($30 vs $25) narrows — or can reverse — in real cost.
• Opus's cost factor: its standard unit price is held steady and cheap, plus there are operational options like fast mode (about 2.5x faster). On the other hand, its "narrate-then-code" tendency tends to increase output tokens.

The bottom line: the price table alone doesn't settle it. For output-heavy coding, the right move is to estimate the total as "unit price × output volume," and to measure and compare per workload. It's a tug-of-war — Opus is cheaper on nominal unit price, while Sol has improved token efficiency.

* A specific "real-cost multiplier" can't be asserted, because neither company discloses an output-token comparison under identical conditions. We recommend measuring both models' output token counts on your own representative tasks and multiplying by unit price to compare.

6. Strengths and weaknesses map

7. How to choose by use case

8. Migration and dual-use strategy

The realistic answer is that "splitting by task" optimizes both cost and quality better than "standardizing on one."

Pattern A. Dual-vendor operation (recommended)

• Core coding (PRs and fixes in real repos): Opus 4.8
• CLI / terminal automation: GPT-5.6 Sol
• Automating long-horizon business workflows: Sol (or Terra if cost-conscious)
• High-reliability work where errors are costly: Opus 4.8
• Security analysis: Sol

Pattern B. Router approach

Use something like OpenRouter / LiteLLM to classify task types and route dynamically. Set rules — real coding to Opus, agentic work to Sol, cost-sensitive light work to GPT-5.6 Terra — and you can minimize real cost while curbing vendor lock-in. Now that GPT-5.6 comes as three models, it's also easier to use Luna/Terra/Sol in three tiers on the OpenAI side alone.

Pattern C. Single-vendor operation

If data governance means you can't use multiple vendors, choose by your primary use. If you have large existing code assets and coding quality plus reliability is critical, Opus 4.8; if you center on business-workflow automation and terminal agents, GPT-5.6 (Sol as the mainstay, tuning cost with Terra/Luna) is the natural choice.

Summary

• Opus 4.8: strong in real-codebase fixes (SWE-bench Pro 69.2%, ahead of Sol's 64.6% even in OpenAI's table) and honesty. On math (FrontierMath, GPQA Diamond) and long context (GraphWalks 1M), OpenAI's table, which lists both models, puts Sol ahead. Also cheap, with unit price held steady. The craftsman type.
• GPT-5.6 Sol: leads in terminal operation (TerminalBench 88.8%), overall agentic capability (Agents' Last Exam 53.6), token efficiency, and security. Easy to purpose-tune with three models. The generalist type.
• Caution: OpenAI's evaluation table does include GPQA Diamond and FrontierMath, and Sol leads on both (AIME, MMLU, and SWE-bench Verified are missing). On SWE-bench Pro, even OpenAI's own table puts Opus 4.8 ahead, and OpenAI has questioned the benchmark itself.
• Selection criterion is not the overall benchmark score but "which benchmark is closest to your work." Opus for real code fixes and reliability; Sol for terminal, agents, and breadth.
• The realistic answer is dual operation. Splitting by task is the best for both cost and quality.

FAQ

Q1. Which is stronger at coding, GPT-5.6 Sol or Claude Opus 4.8?

It depends on the metric. On SWE-bench Pro, which measures bug fixes in real repos, Opus 4.8 tops Sol at 69.2% vs 64.6%. On the other hand, on TerminalBench 2.1, which autonomously operates the terminal, Sol tops Opus at 88.8% vs 78.9%. "Opus for fixing real code, Sol for building with a CLI or agent" is the practical division.

Q2. Which is cheaper?

On nominal unit price, Opus 4.8 is $25 on output and Sol is $30, so Opus is cheaper (input is $5 for both — though Sol is $4 during the promotional period). But since Sol improved coding token efficiency by 54%, real cost can narrow — or reverse — depending on output volume. Measuring output tokens on your own representative tasks and comparing totals is the sure way.

Q3. Is Sol's SWE-bench Pro figure of "64.6%" official?

Yes. It is the value in the evaluation table OpenAI published with GPT-5.6. At the same time, OpenAI published an analysis estimating that about 30% of SWE-bench Pro tasks are broken, questioning the benchmark itself. GPQA Diamond and FrontierMath, which Vellum reported as unpublished right after launch, do appear in the evaluation table as checked on September 22, 2026, and Sol leads on both (GPQA: Sol 94.6%, Opus 4.8 92%). What the table lacks is SWE-bench Verified, AIME, and MMLU.

Q4. For accuracy-critical work like healthcare, legal, and finance, which is better?

Opus 4.8. Its design emphasizes honesty: Anthropic's announcement says it is around four times less likely than its predecessor to let flaws in code it has written pass unremarked, and the Transparency Hub puts its rate of failing to tell the user about important failures at 3.7%. That suits work where the cost of an error is high. On prompt injection, Anthropic reports a 0.4% attack success rate in Gray Swan's one-week public attack competition (the same as Opus 4.7; GPT-5.5 was 1.6%). Paths that handle external input should still have separate guards.

Q5. How do GPT-5.6's other models (Terra/Luna) besides "Sol" fit in?

GPT-5.6 is three models: Luna (fast, low-cost) / Terra (balanced) / Sol (top tier). This article took up Sol as a comparison of the flagships at the time of writing. If you're cost-conscious, Terra offers GPT-5.5-equivalent capability at half the price, so comparing Opus 4.8 with Terra is also a strong option in practice. For details, see the complete GPT-5.6 release guide.

Q6. Is dual (dual-vendor) operation realistic?

It's realistic — in fact recommended. Route with a router — real coding to Opus 4.8, terminal/agent automation to Sol, light work to Terra — and you can have both cost and quality. It also helps you avoid vendor lock-in.

Q7. How should general users (ChatGPT / Claude.ai) choose?

Deciding by primary use is the natural path. For accurate code fixes, Claude.ai (Opus 4.8 at the time of writing); for terminal-operating agents, voice, and ChatGPT-ecosystem integration, ChatGPT (GPT-5.6 at the time of writing). If you won't subscribe to both, picking the one closest to the work you do most avoids mismatches.

Related articles

• Complete GPT-5.6 release guide — details on the Luna/Terra/Sol three-model lineup
• Complete Claude Opus 4.8 release guide — new features, benchmarks, pricing
• GPT-5.5 vs Claude Opus 4.7 in-depth comparison — the previous-generation matchup
• Claude vs ChatGPT price comparison — differences in plan structure
• How Much Does AI Cut Development Effort? — agentic-era data on how much AI cuts dev effort
• Claude Opus 5 release guide — performance, pricing, and two breaking changes
• GPT-5.6 vs GPT-5.5: In-Depth Comparison — 3 Models, Terra at 40% of the Price, Benchmarks and Migration