Model comparison
Claude Opus 5 vs GPT-5.6 Sol
Anthropic against OpenAI, compared on context, price, and verified benchmark results.
Catalog record checked August 10, 2026; individual provider fields may change.
Anthropic
Claude Opus 5
Frontier
OpenAI
GPT-5.6 Sol
Frontier
| Specification | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Tier | Frontier | Frontier |
| Context window | 1M | Winner: 1.05M |
| Max output | 128K | 128K |
| Input / 1M tokens | $5 | $5 |
| Output / 1M tokens | Winner: $25 | $30 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | low, medium, high, xhigh, max | none, low, medium, high, xhigh, max |
| Modalities | text, image | text, image |
| Released | July 24, 2026 | July 9, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-14) | Winner: 63 | 61 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GPT-5.6 Sol: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Claude Opus 5
Anthropic's frontier tier, and ahead of Fable 5 on both recorded benchmarks at half the price.
Best for
- Agentic coding
- Long-context analysis
- Multi-step engineering work
Watch out
Output tokens cost five times input — long generations dominate the bill.
GPT-5.6 Sol
OpenAI's flagship tier, aimed at the hardest reasoning and agentic work.
Best for
- Complex reasoning
- Agentic workflows
- Hard coding tasks
Watch out
25x Luna's input price — overspecified for routine generation. Sol Fast mode (where offered) is roughly ~2× price for ~2.5× speed — only enable when latency is the constraint. `gpt-5.2-chat-latest` / `gpt-5.3-chat-latest` shut down 2026-08-10 — migrate API chat traffic to Sol. ChatGPT Plus/Pro Chat got an Aug 6 Sol refresh + effort slider; Work/Codex/API July builds are unchanged.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
Claude Opus 5 vs GPT-5.6 Sol
Answered from the verified figures on this page rather than general guidance.
Is Claude Opus 5 or GPT-5.6 Sol cheaper for input?
Both cost $5 per million input tokens at standard rates, so input price is not a deciding factor between them.
Is Claude Opus 5 or GPT-5.6 Sol cheaper for output?
Claude Opus 5 is cheaper at $25 per million output tokens, against $30 for GPT-5.6 Sol — roughly 1.2× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
Which has the larger context window, Claude Opus 5 or GPT-5.6 Sol?
GPT-5.6 Sol accepts 1.05M tokens against 1M for Claude Opus 5. This only matters if you routinely send very long documents or large codebases.
Do Claude Opus 5 and GPT-5.6 Sol support the same reasoning levels?
Claude Opus 5 exposes low, medium, high, xhigh, max, while GPT-5.6 Sol exposes none, low, medium, high, xhigh, max.
Should I use Claude Opus 5 or GPT-5.6 Sol?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Claude Opus 5 suits agentic coding; GPT-5.6 Sol suits complex reasoning.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.