Three flagship models, three different bets on what matters most. Claude Opus 4.6 prioritizes depth and safety. GPT-5 aims for broad capability. Gemini 2.5 Pro bets on context length and multimodality.
This comparison uses current official pricing plus practical workflow fit to help you pick the right model for your workload.
If you care more about coding than general flagship positioning, jump from this page to the coding model comparison. If you care more about budget, keep the pricing comparison open too.
Spec Sheet
| Claude Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | |
|---|---|---|---|
| Provider | Anthropic | OpenAI | |
| Context window | 200K tokens | 1.05M tokens | 1M tokens |
| Max output | 32K tokens | 128K tokens | varies by mode |
| Input / 1M tokens | $5.00 | $2.50 | $0.45 |
| Output / 1M tokens | $25.00 | $15.00 | $2.70 |
| Extended thinking | Yes | Yes | Yes |
| Vision | Yes | Yes | Yes |
| Native tool use | Yes | Yes (function calling) | Yes |
| Prompt caching | Explicit (cache_control) | Automatic | Context caching |
Prices are verified against provider pricing pages in April 2026.
Benchmarks That Matter
Coding
Claude still leads on the kind of hard, multi-file work where consistency matters. GPT-5.4 closes much of the practical gap while expanding context and output. Gemini 3.1 Pro usually is not the first pick for the hardest code review, but it becomes attractive when the task spans a huge repository or mixed media.
Reasoning
Reasoning quality is close enough that the real differences are style and cost:
- Claude Opus 4.6 favors depth and caution
- GPT-5.4 favors broad capability and stronger tool workflows
- Gemini 3.1 Pro favors long-context synthesis at a much lower per-token price
Multimodal
Gemini 3.1 Pro has the strongest multimodal story here: long context, search grounding, and broader Google-native integration. Claude and GPT-5.4 both handle images and documents well, but Gemini is the easier fit when the workflow already touches Google Search or mixed media.
Pricing Deep Dive
Cost per 1,000 Typical Conversations
Assuming 2K input + 1K output tokens per conversation:
| Model | Cost per conversation | 1,000 conversations |
|---|---|---|
| Gemini 3.1 Pro | ~$0.0036 | ~$3.60 |
| GPT-5.4 | ~$0.020 | ~$20.00 |
| Claude Opus 4.6 | $0.035 | $35.00 |
Claude Opus 4.6 costs dramatically more than Gemini 3.1 Pro and still notably more than GPT-5.4. The question is whether the quality difference matters enough for the exact step you are running.
Prompt Caching Impact
For applications with repetitive system prompts (chatbots, agents, document analysis), caching changes the economics:
| Model | Standard input | Cached input | Savings |
|---|---|---|---|
| Claude Opus 4.6 | $5.00/1M | $0.50/1M | 90% |
| GPT-5.4 | $2.50/1M | $0.25/1M | 90% |
| Gemini 3.1 Pro | $0.45/1M | varies | varies |
Anthropic's explicit caching gives the deepest discount (90% on cache reads) but requires you to mark cache breakpoints in your prompts. OpenAI's automatic caching is simpler but saves less.
Context Window: When It Actually Matters
Gemini's 1M token context is 5x Claude's and 8x GPT-5's. But context length only matters when you actually use it.
When 1M context matters:
- Analyzing entire codebases (a medium repo is 200K-500K tokens)
- Processing long legal documents or research papers
- Multi-document synthesis (comparing 10+ documents simultaneously)
- Long conversation histories in agent loops
When 200K is enough:
- Most coding tasks (single file or small module)
- Standard chatbot conversations
- Document Q&A on individual files
- API integration and function calling
When 128K is enough:
- Simple chat applications
- Code generation for individual functions
- Most RAG pipelines (retrieved chunks are typically 2K-10K tokens)
For the majority of production applications, 128K is sufficient. The 1M context is a genuine advantage for specific workloads, not a general improvement.
Strengths by Use Case
Claude Opus 4.6 Wins At
Complex coding tasks. The SWE-Bench lead translates to real-world performance on multi-file refactoring, code review, and architecture decisions. If you're using Claude Code or Cursor with Claude, the quality difference is noticeable on hard problems.
Nuanced analysis. Claude tends to produce more balanced, carefully reasoned responses on ambiguous questions. It's less likely to confidently state incorrect information.
Safety-critical applications. Anthropic's Constitutional AI training makes Claude more cautious about edge cases, which is valuable in healthcare, legal, and financial applications.
GPT-5.4 Wins At
General-purpose tasks. GPT-5.4 is the most well-rounded premium model in this set. It handles coding, writing, analysis, and tool use with consistently strong quality across domains.
Ecosystem integration. The OpenAI API is the de facto standard. Most tools, frameworks, and tutorials assume OpenAI format. GPT-5 works out of the box with everything.
Speed. GPT-5 typically has lower latency than Claude Opus 4.6, especially for shorter prompts.
Gemini 3.1 Pro Wins At
Long-context tasks. When you need to process 500K+ tokens, Gemini is the only practical option among flagship models.
Multimodal workflows. Native video understanding, audio processing, and Google Search grounding give Gemini capabilities the others lack.
Cost-sensitive applications. At current Gemini 3.1 Pro pricing, Gemini offers the cheapest entry point among the three flagships by a wide margin.
The Practical Recommendation
For most developers in 2026:
- Use GPT-5.4 as your premium generalist default.
- Switch to Claude Opus 4.6 (or Sonnet 4.6) for complex coding and analysis tasks where quality matters more than cost.
- Use Gemini 3.1 Pro when you need long context or multimodal capabilities.
The multi-model approach works best with an aggregator that lets you switch models without changing your integration. LemonData provides 300+ models through a single OpenAI-compatible API key, so switching between Claude, GPT-5.4, and Gemini is a one-line change.
from openai import OpenAI
client = OpenAI(
api_key="sk-lemon-xxx",
base_url="https://api.lemondata.cc/v1"
)
# Same code, different model
for model in ["gpt-5.4", "claude-opus-4-6", "gemini-3.1-pro"]:
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "Explain quantum computing"}]
)
The practical lesson is simple: the flagship choice is rarely permanent. Most teams end up with one premium default, one cheaper operational default, and one long-context or multimodal specialist.
That is why the “winner” question is useful mostly for purchase framing. In production, the better question is which one deserves to be your default, which one deserves to be your specialist, and which one should stay out of the hot path entirely.
Prices verified against current provider pricing pages in April 2026. Model capabilities evolve rapidly, so use this page as a workflow guide rather than a permanent static scorecard.