GPT‑5.6 Sol vs Claude Opus 4.8
They cost the same to read and nearly the same to cache. But Sol has the newer agentic-coding scores, while Opus is cheaper to generate with and has a cleaner 1M-token long-context price. This is the detailed, sourced comparison.
Last updated July 10, 2026 · Published data; caveats included
GPT‑5.6 Sol
Claude Opus 4.8
Price, context, and caching
The headline input prices are identical. That makes the output mix decisive: every 1M output tokens cost $5 more on Sol. Long prompts matter too: Sol’s public API documentation applies a surcharge to requests over 272K input tokens, whereas Anthropic documents standard pricing across Opus 4.8’s full 1M-token context window.
| API property | GPT‑5.6 Sol | Claude Opus 4.8 |
|---|---|---|
| Input / 1M tokens | $5.00 | $5.00 |
| Output / 1M tokens | $30.00 | $25.00 |
| Cached-input read / 1M | $0.50 | $0.50 |
| Cache write / 1M | $6.25 (1.25× input) | $6.25 for 5-minute cache; $10 for 1-hour cache |
| Batch API input / output | $2.50 / $15.00 | $2.50 / $12.50 |
| Context window / max output | 1.05M / 128K | 1M / 128K |
| Long-context policy | Over 272K input: 2× input and 1.5× output for the full request | 1M context at standard pricing |
Benchmark comparison: what the shared table says
OpenAI’s GPT‑5.6 release includes a comparative table for Sol and Opus 4.8. It is the most complete shared dataset available, but it is still a vendor-published comparison. Treat it as meaningful evidence, not as an independent certification; agent harness, effort setting, tool budget, and model version can alter results.
| Evaluation higher is better | GPT‑5.6 Sol | Claude Opus 4.8 | Readout |
|---|---|---|---|
| Artificial Analysis Coding Agent Index v1.1 | 80.0 | 72.5 | Sol +7.5 index points |
| SWE‑bench Pro | 64.6% | 69.2% | Opus +4.6 points |
| DeepSWE v1.1 | 72.7% | 59.0% | Sol +13.7 points |
| Terminal‑Bench 2.1 | 88.8% | 78.9% | Sol +9.9 points |
| Agents’ Last Exam | 52.7% | 45.2% | Sol +7.5 points |
| GDPval‑AA v2 | 1,747.8 Elo | 1,600.1 Elo | Sol +147.7 Elo |
| Artificial Analysis Intelligence Index v4.1 | 58.9 | 55.7 | Sol +3.2 index points |
Beyond coding: professional work, computer use, science
Sol’s lead in OpenAI’s table is not limited to terminal tasks. It also leads the listed professional, browsing, computer-use, and science evaluations. These remain vendor-compiled comparisons, so use them as a starting hypothesis to test against your own workloads.
Professional & computer use
| Evaluation | Sol | Opus |
|---|---|---|
| Management consulting tasks | 43.2% | 31.6% |
| Big Finance Bench | 53.0% | 44.0% |
| OSWorld 2.0 | 62.6% | 54.8% |
| BrowseComp | 90.4% | 84.3% |
| BenchCAD | 70.6% | 51.8% |
Science & academic reasoning
| Evaluation | Sol | Opus |
|---|---|---|
| GeneBench Pro | 28.7% | 16.0% |
| LifeSciBench | 59.9% | 53.6% |
| HealthBench Professional | 60.5% | 53.0% |
| GPQA Diamond | 94.6% | 92.0% |
| FrontierMath Tier 1–3 | 89.0% | 80.0% |
Long context and tool orchestration: Opus’s important counterpoints
Long context: in OpenAI’s table, Sol leads Opus on GraphWalks BFS at 256K (90.7% vs 85.9%) and 1M (77.1% vs 68.1%). But pricing matters operationally: Opus’s full 1M window is standard priced, while Sol’s documented surcharge begins at 272K input tokens.
| Long context / tool use | GPT‑5.6 Sol | Claude Opus 4.8 | Important qualifier |
|---|---|---|---|
| GraphWalks BFS 256K | 90.7% | 85.9% | From OpenAI’s comparison table |
| GraphWalks BFS 1M | 77.1% | 68.1% | From OpenAI’s comparison table |
| MCP Atlas | Not published | 82.2% | Anthropic evaluation configuration |
| Toolathlon | 58.0% | 59.9% | From OpenAI’s comparison table |
Radar: a decision map, not an “IQ score”
The radar only summarizes transparent dimensions above: Terminal‑Bench, SWE‑bench Pro, coding-agent index, output-price efficiency, and MCP Atlas. It is not a weighted quality metric. A missing Sol MCP Atlas score remains missing; it is not filled in by a proxy.
● GPT‑5.6 Sol ● Claude Opus 4.8
- Sol’s large shape comes from published terminal and coding-agent scores.
- Opus reaches farther on SWE‑bench Pro, output economics, and the published MCP Atlas dimension.
- The radar should guide an evaluation plan, not replace it.
Which should you use?
You use Codex or the Responses API; terminal-agent performance is the bottleneck; and Sol’s stronger published agentic-coding results justify higher output cost. Sol also offers max reasoning and ultra, which coordinates subagents for demanding work.
Outputs are large, prompts regularly approach 1M tokens, or MCP orchestration is central. Opus is cheaper to generate with, has standard 1M-context pricing, and has a published MCP Atlas result. It is also broadly available through Claude, AWS, Google Cloud, and Microsoft Foundry.
You need production confidence. Test both on held-out tickets from your repositories, tracking task success, tool-call errors, time-to-merge, total billed tokens, and review burden. A public benchmark cannot model your repo conventions or permission boundary.
Final take
Sol is the current published leader for agentic coding. It leads in the common comparison table on coding-agent index, DeepSWE, and Terminal‑Bench, and it also leads most of the professional and scientific results listed there. Opus 4.8 remains a first-class frontier alternative rather than a fallback. It wins the shared SWE‑bench Pro result, reduces output cost, keeps 1M context at standard rates, and is the only one of the two with a published MCP Atlas score.
Choose Sol for coding-agent throughput and tool-driven engineering. Choose Opus for output-heavy, long-context, or MCP-centric workflows. For a company-wide default, the right answer is usually a router backed by your own evals—not a single benchmark crown.
Sources & methodology
- OpenAI — GPT‑5.6 launch and full evaluation tables: Sol prices, model specs, and the shared Sol/Opus benchmark figures used above.
- OpenAI API docs — GPT‑5.6 Sol: 1.05M context, 128K output, cache policy, and long-context surcharge.
- Anthropic — Claude Opus 4.8: $5/$25 pricing, availability, caching, and batch discount.
- Anthropic pricing documentation: prompt-cache write rates and standard long-context pricing.
- Claude Opus 4.8 System Card: methodology and published Opus evaluation results, including MCP Atlas.
- Artificial Analysis — GPT‑5.6 benchmarks across intelligence, speed and cost: independent Coding Agent Index commentary and task-cost observations.