Claude Opus 4.8 vs GPT-5.6 Sol
Claude Opus 4.8 wins on Overall, Multimodal; GPT-5.6 Sol wins on Coding. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose Claude Opus 4.8 if you…
- Previous flagship still capable
- Well-proven stability
- Best for: Deep reasoning, Long documents, Coding
Choose GPT-5.6 Sol if you…
- Enhanced reasoning
- Strong code generation
- o-series architecture
- Best for: Reasoning-heavy tasks, Coding, Math
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | Claude Opus 4.8 | GPT-5.6 Sol | Verdict |
|---|---|---|---|
| Vendor | Anthropic (US) | OpenAI (US) | Different vendors |
| Released | 2026.05 | 2026.07 | GPT-5.6 Sol is newer |
| Overall (rank) | 81 · #5 | 72 · #11 | Claude Opus 4.8 +9 |
| Coding | 87 · #4 | 88 · #3 | GPT-5.6 Sol +1 |
| Multimodal | 89 · #6 | 80 · #11 | Claude Opus 4.8 +9 |
| Context window | 1M | 1.05M | GPT-5.6 Sol larger |
| Max output | 128K | 128K | Tie |
| Effective-context | 96 | 88 | Claude Opus 4.8 more reliable |
| Input $/1M | $5 | $5 | Tie |
| Output $/1M | $25 | $30 | Claude Opus 4.8 cheaper |
| Cache discount | 90% off | 50% off | Claude Opus 4.8 deeper |
| Speed | ~40 tok/s | ~25 tok/s | Claude Opus 4.8 faster |
| TTFT | 1.0s | 2.0s | Claude Opus 4.8 snappier |
| Function calling | 88 | 93 | GPT-5.6 Sol ahead |
| Refusal rate | ~9% | ~14% | Claude Opus 4.8 less restrictive |
| English | 94 | 92 | Claude Opus 4.8 |
| Chinese | 78 | 74 | Claude Opus 4.8 |
| Modalities | text, image | text, image | Same |
| Open weights | No | No | Both closed |
| Fine-tuning | No | No | |
| Free tier | No free tier | No free API tier | |
| SOC2 / no-train | yes / yes | yes / yes | |
| Private deployment | No | No | Neither |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
Claude Opus 4.8 leads the overall aggregate by 9 points (81 vs 72). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GPT-5.6 Sol remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a modest gap: GPT-5.6 Sol scores 88 against 87. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.6 Sol needs fewer correction turns; Claude Opus 4.8 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
Claude Opus 4.8 leads multimodal 89 vs 80. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
Claude Opus 4.8 is faster in interactive use: ~40 tok/s with 1.0s TTFT versus ~25 tok/s with 2.0s TTFT (about 1.6× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1M for Claude Opus 4.8 and 1.05M for GPT-5.6 Sol. Crucially, the larger nominal window does not win on usable recall: GPT-5.6 Sol advertises 1.05M but Claude Opus 4.8 scores higher on effective-context (96 vs 88), i.e. it actually retains more of what it was given.
Price & total cost
Claude Opus 4.8 is the cheaper API at $5/$25 versus GPT-5.6 Sol at $5/$30 per 1M input/output tokens — list input is about 1.0× lower. Cache discounts (Claude Opus 4.8 90% vs GPT-5.6 Sol 50%) shift the effective bill, worked out below.
Chinese vs English
English: Claude Opus 4.8 94 vs GPT-5.6 Sol 92. Chinese: 78 vs 74. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.
Tool use & ecosystem
Function-calling score: Claude Opus 4.8 88 vs GPT-5.6 Sol 93, so GPT-5.6 Sol has the edge on structured tool use. Neither offers standard fine-tuning. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | Claude Opus 4.8 | GPT-5.6 Sol | Gap |
|---|---|---|---|
| List priceno cache applied | $1,250100M in × $5 + 30M out × $25 | $1,400100M in × $5 + 30M out × $30 | 1.12×gap |
| With caching90% of inputs cache-hit | $84590M cached in × $0.5 + 10M in × $5 + 30M out × $25 | $1,17590M cached in × $2.5 + 10M in × $5 + 30M out × $30 | 1.39×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.