Tech Comparisons
AI Token Limits Compared — GPT-5.5 vs Claude vs Gemini 3.1 Pro
Token limits determine how much text an AI can read at once, how much it costs, and what tasks it can handle. Here's how the latest models compare in May 2026.
| Feature | AI Token Limits Compared — GPT-5.5 | Claude | Gemini 3.1 Pro |
|---|---|---|---|
| Model & Context Window | GPT-5.5: context window not yet officially confirmed by OpenAI. | Claude Sonnet 4.6: 200K tokens · Claude Opus 4.6+: 1M tokens. | Gemini 3.1 Pro: 2,000,000 tokens — largest available anywhere. |
| Input Cost / 1M tokens | GPT-5.5: higher than GPT-5.4 (exact rate not yet published). | $3.00 (Claude Sonnet 4.6) · $15.00 (Claude Opus 4.7). | $2.00 ≤200K · $4.00 >200K (Gemini 3.1 Pro). |
| Output Cost / 1M tokens | GPT-5.5: higher than predecessors (exact rate TBC). | $15.00 (Claude Sonnet 4.6) · $75.00 (Claude Opus 4.7). | $12.00 (Gemini 3.1 Pro). |
| Prompt Caching | 50% off on cached input tokens (automatic). | 90% off on cached input tokens (explicit cache_control). | Automatic caching — discount varies by usage. |
| Batch API Discount | 50% off — async batch processing. | 50% off — async batch processing. | Available via Vertex AI batch prediction. |
| What fits in context | GPT-5.5: TBC — previous GPT-4o fit ~200 pages / ~5K lines. | 200K: ~460 pages · 1M: ~2,300 pages / entire medium codebases. | 2M tokens: ~5,000 pages, entire large codebases, hours of video transcript. |
| Best For | Agentic coding, computer use, broad OpenAI-compatible apps. | Legal docs, large codebases, high-quality output, long conversations. | Entire repo analysis, very long documents, video, cost-sensitive high-volume. |
AI Token Limits Compared — GPT-5.5 Pros & Cons
Pros
- GPT-5.5 most capable for agentic coding and computer use
- Widest API compatibility — works with every AI framework
- More token-efficient than GPT-5.4 at similar latency
- Fastest ecosystem tooling and examples
Cons
- Context window and exact pricing not yet published for GPT-5.5
- Pricing higher than predecessor models
- No 1M+ token option confirmed
Claude Pros & Cons
Pros
- Best prompt caching — 90% off repeated system prompts
- Up to 1M tokens on Opus — whole repo fits in one prompt
- Extended thinking tokens for complex reasoning
- Most predictable output quality for production use
Cons
- Opus very expensive ($75/M output)
- Sonnet capped at 200K — need Opus for larger context
- No video understanding
Verdict
Gemini 3.1 Pro's 2M token context is transformative for tasks like whole-repo analysis, processing entire books, or handling long video transcripts — and its pricing is competitive at $2/$12 per 1M tokens. Claude Sonnet's 200K + 90% prompt caching makes it most cost-effective for repeated long system prompts. GPT-5.5 is the strongest for agentic coding but context window details are still unconfirmed. Match the model to your actual prompt sizes: pay for context you'll use, not context you won't.