LLM context window comparison (2026)
From GPT-4o's 128K to Gemini 2.5 Pro's 2M tokens — context length has stratified by an order of magnitude. Below: what each tier actually fits, what it costs, and where recall breaks down.
Every major LLM by context size
| Model | Context (tokens) | Effective recall | Input cost / 1M |
|---|---|---|---|
| Gemini 2.5 Pro | 2,000,000 | Strong to ~1M, degrades after | $1.25 |
| Claude Sonnet 4.5 (enterprise) | 1,000,000 | Strong to ~800K | $3 (tier-dependent) |
| Gemini 2.5 Flash | 1,000,000 | Uneven above 500K | $0.075 |
| AI21 Jamba 1.5 Large | 256,000 | Excellent (Mamba hybrid) | $2 |
| Claude Sonnet 4.5 (default) | 200,000 | Strong to 200K | $3 |
| Claude Haiku 4.5 | 200,000 | Strong to 200K | $1 |
| GPT-4o | 128,000 | Strong to 128K | $2.50 |
| GPT-4o mini | 128,000 | Strong to 128K | $0.15 |
| Mistral Large 2 | 128,000 | Strong to 128K | $2 |
| Llama 3.3 70B | 128,000 | Uneven above 64K | $0.88 |
VerticalAPI verdict
Match context size to actual document size, not aspiration. GPT-4o's 128K handles most RAG and chat. Claude's 200K is the sweet spot for codebases and long contracts. Gemini's 1M-2M is for full-corpus analysis (whole repos, legal discovery, long meeting archives) where you would otherwise build a RAG pipeline. AI21 Jamba's Mamba architecture is the strongest 256K recall in 2026. Through VerticalAPI BYOK you can route per-document-size with one model parameter.
Frequently asked questions
Which LLM has the largest context window in 2026?
Gemini 2.5 Pro has the largest publicly accessible context window in 2026 at 2 million tokens. AI21 Jamba 1.5 Large supports 256K tokens with strong recall thanks to its Mamba-Transformer hybrid architecture. Claude Sonnet 4.5 ships 200K tokens by default and 1M tokens on enterprise tiers. GPT-4o is at 128K tokens. For pure max context capacity, Gemini 2.5 Pro is the leader by a wide margin; for cost-effective long context with reliable recall, Claude's 200K is the most common production pick.
How many tokens is 1 million? What does it fit?
1 million tokens is roughly 750,000 English words, or about 1,500 pages of dense text. Practical fits: an entire 800-page novel, a full software codebase of 30-50K lines, 6 hours of meeting transcripts, or a year of email correspondence. 2M tokens (Gemini) fits multiple large codebases or several legal contract batches in one prompt. 200K tokens (Claude default) handles a 300-page document or 8-10K lines of code comfortably. 128K (GPT-4o) is enough for most RAG and chat workloads but not full-codebase analysis.
Does long context actually work, or does recall degrade?
All long-context LLMs degrade on recall beyond a fraction of their stated window. Needle-in-a-haystack tests show Claude maintains strong recall to 200K, Gemini 2.5 Pro to about 1M before noticeable degradation, and GPT-4o is generally reliable to 128K. AI21 Jamba's Mamba hybrid is competitive at 256K with smoother recall curves than pure Transformer models. For production, treat the effective usable window as 60-80% of the advertised maximum, especially for multi-step reasoning over the full context rather than simple lookup.
What is the cost of using long context?
Long context is expensive per request because you pay for every input token. A 1M-token Gemini 2.5 Pro request at $1.25/M input costs $1.25 just for input — before any output. A 200K Claude Sonnet 4.5 request at $3/M costs $0.60 input. Prompt caching (Anthropic, OpenAI, Gemini context caching) dramatically reduces cost on reused prefixes, often by 50-90%. For workloads with stable system prompts and rotating user queries, caching makes long context economically viable; without caching, RAG with selective retrieval is usually cheaper at scale.
Can I switch between long-context LLMs through one API?
Yes. VerticalAPI exposes Gemini 2.5 Pro (2M), Claude Sonnet 4.5 (200K-1M), GPT-4o (128K), and AI21 Jamba (256K) through a single OpenAI-compatible endpoint at https://api.verticalapi.com/v1. You change the model parameter and matching X-Provider-Key header. BYOK means you pay each provider directly at list price — zero markup. For long-document pipelines you can route by document size: short to GPT-4o, medium to Claude, very large to Gemini, all from the same SDK.
Limitations of this comparison
- Stated context windows are maximum input only — output tokens count separately and reduce effective room for input.
- Recall tests vary by methodology; some labs report aggressive numbers that don't survive multi-step reasoning over the full context.
- Long-context latency is higher: 1M-token Gemini requests can take 10-30 seconds even with streaming.
- Enterprise tiers (Claude 1M) require sales contact and have minimum spend, not openly priced.
- Some open-weight inference providers report 128K but degrade noticeably above 32-64K depending on quantization.
What may change in 12-24 months
- OpenAI is expected to ship a 1M-context GPT-4o successor in late 2026 to match Gemini and Claude.
- Mamba-Transformer hybrids (AI21, Jamba) may make 1M+ context cheaper and faster than pure Transformer models.
- Better long-context attention may make full-corpus analysis preferable to RAG for many use cases by 2027.
- Context caching pricing is expected to converge across vendors, removing one of the main cost-comparison frictions.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- When should I use long context instead of RAG?
- Does Claude 1M context actually work for codebase review?
- How much does Gemini 2.5 Pro cost for full-codebase analysis?
- What is the cheapest long-context LLM in 2026?
- Does AI21 Jamba beat Claude on long-context recall?
More LLM comparisons
Production picks for 100K+ token workloads
Full 2026 pricing matrix
Make long context economical
Claude vs Gemini head-to-head
Retrieval-augmented production picks