Claude Opus 4.6 vs GPT-5
Claude Opus 4.6 (2026) and GPT-5 (2025) are frontier-tier reasoning models from Anthropic and OpenAI. Claude Opus 4.6 ships a 1M-token context window, while GPT-5 ships a 400K-token context window. On SWE-bench Verified, Claude Opus 4.6 leads by 5.9 pts. On pricing, GPT-5 costs $1.25/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
GPT-5 is ~300% cheaper at $1.25/1M; pay for Claude Opus 4.6 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Claude Opus 4.6 | GPT-5 |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1M | 400K |
| Cheapest output | $25/1M tokens | $10/1M tokens |
| Provider routes | 4 tracked | 3 tracked |
| Shared benchmarks | SWE-bench Verified leader | 1 rows |
Decision tradeoffs
- Claude Opus 4.6 leads the largest shared benchmark signal on SWE-bench Verified by 5.9 points.
- Claude Opus 4.6 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Claude Opus 4.6 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Claude Opus 4.6 for Coding, RAG, and Agents.
- GPT-5 has the lower cheapest tracked output price at $10/1M tokens.
- Local decision data tags GPT-5 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Claude Opus 4.6
$10,250
Cheapest tracked route: Anthropic
GPT-5
$3,500
Cheapest tracked route: Replicate API
Estimated monthly gap: $6,750. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GPT-5 is $15/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Claude Opus 4.6 is $15/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-08-07 |
| Context window | 1M | 400K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-12 | 2024-09 |
Pricing and availability
| Pricing attribute | Claude Opus 4.6 | GPT-5 |
|---|---|---|
| Input price | $5/1M tokens | $1.25/1M tokens |
| Output price | $25/1M tokens | $10/1M tokens |
| Providers |
Capabilities
| Capability | Claude Opus 4.6 | GPT-5 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
Benchmarks
| Benchmark | Claude Opus 4.6 | GPT-5 |
|---|---|---|
| SWE-bench Verified | 80.8 | 74.9 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has Claude Opus 4.6 at 80.8 and GPT-5 at 74.9, with Claude Opus 4.6 ahead by 5.9 points. The largest visible gap is 5.9 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint is close: both models cover vision, multimodal input, reasoning mode, function calling, and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Claude Opus 4.6 lists $5/1M input and $25/1M output tokens, while GPT-5 lists $1.25/1M input and $10/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-5 lower by about $7.13 per million blended tokens. Availability is 4 providers versus 3, so concentration risk also matters.
Choose Claude Opus 4.6 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose GPT-5 when coding workflow support and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Claude Opus 4.6 or GPT-5?
Claude Opus 4.6 supports 1M tokens, while GPT-5 supports 400K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Claude Opus 4.6 or GPT-5?
GPT-5 is cheaper on tracked token pricing. Claude Opus 4.6 costs $5/1M input and $25/1M output tokens. GPT-5 costs $1.25/1M input and $10/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Claude Opus 4.6 or GPT-5 open source?
Claude Opus 4.6 is listed under Proprietary. GPT-5 is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Claude Opus 4.6 or GPT-5?
Both Claude Opus 4.6 and GPT-5 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Claude Opus 4.6 or GPT-5?
Both Claude Opus 4.6 and GPT-5 expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run Claude Opus 4.6 and GPT-5?
Claude Opus 4.6 is available on OpenRouter, Anthropic, AWS Bedrock, and GCP Vertex AI. GPT-5 is available on Replicate API, OpenRouter, and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.