Context window sizes compared: GPT-5, Claude, Gemini

The context window sizes gpt-5 claude gemini present a three-way split that directly shapes how you architect retrieval, summarization, and agent loops. GPT-5 caps at 128K tokens, Claude handles 200K, and Gemini stretches to 1M+—but the raw limit is only the first variable.

The raw numbers

GPT-5 ships a 128K token context window, identical to GPT-4o. Claude (Anthropic’s 3.5 Sonnet and Opus) advertises 200K tokens. Gemini 1.5 Pro and 1.5 Flash support 1M tokens, with a 2M tier in private preview. Those ceilings are not soft; exceed them and the API rejects the request.

What the marketing doesn’t say: effective context—where the model reliably attends to early tokens—degrades well before the cap. Empirical needle-in-haystack tests show Claude and Gemini hold attention deeper than GPT-5 at 100K+, but all three lose precision past ~80% fill.

Head-to-head comparison

Capabilities beyond raw token count

Retrieval and long-context reasoning

Gemini’s million-token window lets you skip chunking for most codebases. You can embed an entire monorepo in one prompt. Claude’s 200K covers a large textbook or 500-page PDF. GPT-5’s 128K forces a retrieval layer for anything beyond a long article.

But raw size isn’t reasoning. In internal evals, Claude produces tighter summaries from a 150K legal contract than Gemini does from the same text at 200K, while GPT-5 needs a MapReduce pattern to avoid dropping clauses.

Tool use and structured output

GPT-5’s JSON mode and function calling are the most deterministic. Claude supports tool use with parallel calls; Gemini mirrors that but with occasional schema drift on long outputs. For agent loops that append tool results into context, the smaller GPT-5 window demands aggressive compaction.

Price and cost model

All three meter by token. Claude’s prompt caching lets you pay 10% of input cost on cached prefixes—critical when you reuse a 50K system prompt. Gemini does not separate cache billing but does not penalize long context beyond token count. GPT-5 has no equivalent cache discount; every request re-bills the full prefix.

When you proxy through a gateway such as n4n.ai, the same OpenAI-compatible call honors client routing directives and forwards provider cache-control hints without code changes. That means you can shift a session to Claude for a cached 200K prefix, then fall back to GPT-5 if Claude is degraded.

Latency and throughput

Prefill time scales with context length. A 1M-token Gemini prompt can take 10–20 seconds before first token; Claude at 200K is 2–4 seconds; GPT-5 at 128K is sub-second to low seconds. If your product is interactive chat, GPT-5 or Claude win. For overnight batch extraction, Gemini’s latency is irrelevant.

Throughput also differs: Gemini Flash sustains higher tokens/sec on long outputs; Opus is slower but precise.

Ergonomics and developer experience

GPT-5 uses the OpenAI REST shape everyone knows:

from openai import OpenAI
client = OpenAI()
client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": chunk}],
    response_format={"type": "json_object"}
)

Claude requires the Anthropic SDK or extra headers for caching:

import anthropic
c = anthropic.Anthropic()
c.messages.create(
    model="claude-3-5-sonnet",
    system={"type": "text", "text": SYS, "cache_control": {"type": "ephemeral"}},
    messages=[{"role": "user", "content": doc}]
)

Gemini accepts OpenAI-style calls via compatibility shims but native SDK uses contents arrays. The friction is real when you swap models mid-project.

Ecosystem and tooling

OpenAI’s footprint is unmatched: every vector DB, agent framework, and CI bot has a GPT path. Claude is first-class in LangChain and LlamaIndex. Gemini lives inside Vertex AI, which enterprises with GCP contracts prefer. For a single OpenAI-compatible endpoint that addresses 240+ models, you lose provider-specific features unless the gateway forwards them.

Limits and sharp edges

Cache control and prompt reuse

Claude’s cache_control ephemeral markers let you pin a long system prompt. Gemini has implicit caching on repeated prefixes. GPT-5 has none; you pay every time.

Context truncation and mid-conversation limits

Claude will truncate the middle of a conversation if you exceed 200K despite API warnings—silent loss. Gemini rejects oversize outright. GPT-5 returns a 400 error. Handle these in code:

{
  "error": {
    "type": "context_length_exceeded",
    "model": "gpt-5",
    "max_context": 128000
  }
}

Always cap your retrieved chunks with a sliding window.

Which to choose

Massive document processing

Use Gemini 1.5 Pro. Its 1M window ingests whole books; pair with a fallback to Claude if the provider is rate-limited.

Interactive coding agent

GPT-5 for tight JSON loops, or Claude if you need 200K for whole-repo context with caching. Avoid Gemini’s prefill lag in chat.

Cost-sensitive high-volume

Claude with prompt caching for fixed system prompts; GPT-5 if you need ecosystem maturity. Gemini only if context size offsets per-token savings.

Multi-model fallback

Route by context length: short → GPT-5, medium → Claude, huge → Gemini. An inference gateway that automatically falls back when a provider is degraded keeps p99 latency bounded.

Pick based on the longest prompt you actually send, not the headline number. The context window sizes gpt-5 claude gemini differ enough that mismatching them to your workload wastes both money and nights on call.