GPT-5 vs Claude Opus 4.7 vs Gemini 2.5 Pro: 2026 Capability Matrix
GPT-5, Claude Opus 4.7, and Gemini 2.5 Pro are the three flagship frontier models of 2026. They occupy overlapping but distinct positions — each is best-in-class at something, and none dominates the others across the board. This is the working matrix for choosing between them.
Last reviewed:
What we're comparing
OpenAI's general-purpose flagship (Mar 2026). Multimodal, reasoning-capable, mid-price frontier tier.
Anthropic's premium flagship (Feb 2026). 1M context, extended thinking, best-in-class on long-context reasoning and agentic tool use.
Google's multimodal flagship (Feb 2026). 2M context, native audio+video, grounded search integration.
Side-by-side
| Attribute | GPT-5 | Claude Opus 4.7 | Gemini 2.5 Pro |
|---|---|---|---|
| Context window | 1M tokens | 1M tokens | 2M tokens |
| Max output | 32K tokens | 32K tokens | 65K tokens |
| Input $/M (list) | $10 | $15 | $3.50 |
| Output $/M (list) | $30 | $75 | $15 |
| Cached input $/M | $1 | $1.50 | $0.90 |
| MMLU-Pro | 86 | 87 | 84 |
| GPQA Diamond | 82 | 84 | 80 |
| SWE-Bench Verified | 70 | 72 | 63 |
| Modalities | Text, image, audio | Text, image | Text, image, audio, video |
| Reasoning mode | Yes (native) | Yes (extended thinking) | Yes (deep think) |
| Agentic tool use | Strong | Best-in-class | Strong |
| Best-fit workload | General purpose w/ multimodal | Long-context coding, agents | Grounded search, video/audio, ultra-long-context |
When to use which
- Use GPT-5
Multimodal (image + audio input) general workloads with balanced price-quality. Strong default when you need one model that does most things well.
- Use Claude Opus 4.7
Long-context reasoning, coding, and production agents. Best when quality matters more than cost and you need tool-calling to just work.
- Use Gemini 2.5 Pro
Ultra-long context (>1M), video / audio input, or when you want grounded search results baked in. Lowest list price of the three.
FAQs
Which frontier model is cheapest at scale?
Gemini 2.5 Pro on list price. With prompt caching, all three drop input cost 10×, so per-workload cost depends more on your caching ratio and output volume than headline price.
Which is best at coding?
Claude Opus 4.7 leads on SWE-Bench Verified (72 vs GPT-5's 70 vs Gemini 2.5 Pro's 63) and is the coding default in most 2026 IDE integrations.
Can I switch between them mid-workflow?
Yes, via a router (LLM gateway or your own logic). Route reasoning-heavy subtasks to premium tier, simple classifications to Haiku / Flash / GPT-5 mini. This is the highest-leverage cost optimization for most teams.
Is Gemini's 2M context real or effective?
Nominal is 2M. Effective retrieval accuracy across the full window degrades past ~500K tokens on real-world tasks (needle-in-haystack + reasoning), consistent with all frontier models. Treat 2M as a compressibility budget, not a working memory.