Context window size compared across LLM API providers

Doing a context window size LLM API comparison in 2024 means looking past the marketing numbers to how each provider handles long inputs in practice. OpenAI, Anthropic, and Google publish different maxima, but the effective window for retrieval-augmented generation depends on chunking, caching, and output limits.

Providers and published windows

OpenAI

GPT-4o and GPT-4 Turbo expose a 128,000-token context window. The output cap sits at 16,384 tokens for GPT-4o. Practical RAG workloads rarely fill the input because retrieval scores degrade and cost scales linearly with input tokens. The API accepts max_tokens to bound output, and system messages count against the limit.

Anthropic

Claude 3.5 Sonnet and Claude 3 Opus advertise 200,000 tokens of context. Output max is 8,192 tokens. Anthropic’s research shows strong recall at 100k+ tokens, which matters when you dump entire codebases or long PDFs into a single prompt. The cache_control field lets you mark a prefix as cacheable.

Google

Gemini 1.5 Pro ships a 1,000,000-token window (2M in private preview). Output caps at 8,192 tokens. The long context is genuine dense attention, not sparse approximation, but throughput suffers at the extreme end. The contents schema separates user and model turns.

Cohere

Command R+ provides 128,000 tokens with a focus on enterprise RAG. It includes native reranking endpoints, which changes the pipeline shape. Output max is 4,096 tokens. Pricing is among the lowest for input tokens.

Capabilities: what the window actually buys you

A larger context window is not a substitute for retrieval quality. In a RAG pipeline you still chunk documents; the context window determines how many chunks you can stuff into a single prompt before hitting limits. Throughout this context window size LLM API comparison we have focused on RAG because that is where the window is exercised hardest.

Anthropic and Google handle long concatenated contexts with less loss than OpenAI in independent evaluations, but your mileage depends on instruction placement. Put the task at the top and bottom of the prompt, not the middle.

Tokenization differs per provider. OpenAI uses cl100k, Anthropic uses a custom tokenizer, Google uses SentencePiece. A 10k-character English chunk is ~2.5k tokens on OpenAI, ~3k on Anthropic. Estimate before packing.

# Typical RAG assembly with a hard cap
MAX_CTX = 128_000
reserved = 2_000  # for instructions + output
budget = MAX_CTX - reserved
chunks = retrieve(query, top_k=50)
prompt = build_prompt(chunks, budget)

import tiktoken
enc = tiktoken.get_encoding("cl100k_base")
print(len(enc.encode(prompt)))

When you exceed the window, you must either truncate or use iterative retrieval. Truncation loses data; iteration adds latency. For most production RAG, keep the active context under 32k and rely on a reranker to select the best passages.

Price and cost model

Context size directly drives cost because most providers charge per input token. OpenAI prices GPT-4o at $5 per million input, $15 per million output. Anthropic Claude 3.5 Sonnet is $3 per million input, $15 per million output. Gemini 1.5 Pro is $3.50 per million input for the first 128k, then $7 per million beyond. Cohere Command R+ is $2.50 per million input, $10 per million output.

Output tokens cost 3x input on most providers. In RAG, the generated answer is small, but if you use the LLM to synthesize across many chunks, output can grow. Set max_tokens tightly.

Caching changes the math. Anthropic offers prompt caching with 10-minute TTL and 90% discount on cached input. OpenAI supports automatic caching for exact prefix matches on GPT-4 Turbo. Google provides context caching at $1 per million stored tokens per hour.

{
  "anthropic": {
    "cache_control": {"type": "ephemeral"},
    "ttl": "10m",
    "discount": "90%"
  }
}

For high-volume RAG, cached system prompts and repeated document prefixes cut spend more than picking the cheapest base rate. A static system prompt of 2k tokens cached across 1M requests saves $9 on OpenAI versus uncached.

Latency and throughput

Time-to-first-token (TTFT) grows with input length. A 128k prompt processes more tokens through attention than an 8k prompt, so expect slower starts. Gemini at 1M tokens exhibits multi-second TTFT; OpenAI at 128k is faster but still slower than short prompts. Throughput (tokens/sec) also drops as the attention cost scales.

Streaming mitigates perceived latency; the first chunk is still delayed by prefill. If your RAG service needs sub-second interactivity, keep context under 32k regardless of provider. For batch extraction over whole books, Gemini’s million-token window wins despite latency. Measure TTFT in your own region; cold starts vary.

Ergonomics

All four providers support OpenAI-style chat completions, but field names differ. Anthropic uses system as a top-level param and requires max_tokens. Google uses a contents array with parts. Cohere has chat with a documents argument for native RAG, letting you pass retrieved chunks as structured objects.

A gateway normalizes this. n4n.ai presents one OpenAI-compatible endpoint covering 240+ models, forwards provider cache-control hints, and automatically falls back when a provider is rate-limited. That removes the need to branch your client code per provider.

curl https://api.n4n.ai/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -d '{"model":"claude-3-5-sonnet","messages":[...]}'

Streaming works uniformly over SSE, but Anthropic’s stream emits content_block deltas while OpenAI emits delta.content. Normalize in your wrapper. Error shapes also differ: OpenAI returns error.message, Anthropic uses error.type, Google uses error.status.

Ecosystem and limits

OpenAI has the largest tooling ecosystem: function calling, assistants, fine-tuning. Anthropic ships solid tool use and vision. Google ties to Vertex and Duet. Cohere’s rerankers are best-in-class for retrieval.

Hard limits beyond context: OpenAI rate limits by RPM/TPM; Anthropic by tokens per minute; Google by requests per minute. For RAG you hit TPM limits before RPM because each request carries many input tokens. Plan batch windows accordingly. Private deployments exist for all four, but only Cohere and Anthropic emphasize on-prem for regulated RAG.

Comparison table

*Google price tiers beyond 128k differ.

Which to choose

Interactive RAG chat ( < 32k context )

OpenAI GPT-4o or Anthropic Claude. Low latency, good tooling. Use caching for system prompt. Pick Anthropic if you need cheaper input and longer occasional bursts.

Long-document analysis (50k–200k)

Anthropic Claude 3.5 Sonnet. Best recall, 200k window, cheaper than OpenAI at scale. Use cache_control on the document prefix.

Whole-corpus ingestion ( > 500k)

Gemini 1.5 Pro. Only viable million-token option. Accept latency and cache context to cut cost.

Multi-provider production with fallback

Use a gateway like n4n.ai to avoid vendor lock and handle degradation. Keep your chunking logic provider-agnostic and pass routing directives per request.

Tight budget, enterprise reranking

Cohere Command R+ with its rerank endpoint. Pair with a cheap embedding model for hybrid search.

The context window size LLM API comparison shows no single winner. Match the window to the retrieval strategy, not the headline number.