Which frontier model wins long context — Claude Fable 5 or GPT-5.5?

It depends on the task. GPT-5.5 wins long-context retrieval — it scores 74.0% on OpenAI's MRCR v2 benchmark at 512K–1M tokens, a major fix vs GPT-5.4's 36.6% at the same range. Claude Fable 5 wins long-context reasoning and graph-walk benchmarks at the same range. For 'find this fact in 800K tokens' tasks: GPT-5.5. For 'reason across 800K tokens of code': Claude Fable 5.

What is MRCR v2 and why does it matter?

MRCR (Multi-Round Coreference Resolution) v2 is OpenAI's benchmark for testing whether a model can correctly identify which entity is being referred to across a very long context. v2 specifically tests at the 512K–1M token range, which is where GPT-5.4 fell apart (36.6%) and where GPT-5.5 holds up (74.0%). For RAG over million-token documents, MRCR v2 performance is the cleanest indicator of whether the model actually uses the context vs ignoring most of it.

Does Claude Fable 5 really have 1M context?

Yes. Claude Fable 5 ships 1M token context window by default and supports up to 128K output tokens per request. Anthropic released it June 9, 2026 alongside the restricted Claude Mythos 5. Available on Claude API, Claude Platform on AWS, Amazon Bedrock, Vertex AI, and Microsoft Foundry. The 1M window is real and usable, not a demo number.

What about Gemini 3.5 Pro's 2M context?

Gemini 3.5 Pro's 2M token window is the largest of the three frontier models. It is rolling out in June 2026 after the Google I/O 2026 announcement. Independent benchmarks at the full 1.5M–2M range are not yet available. If you need more than 1M tokens in one shot, Gemini 3.5 Pro is the only option. For most workloads, 1M is enough, and you pick between GPT-5.5 (retrieval) and Claude Fable 5 (reasoning).

Quick Answer

Claude Fable 5 1M Context vs GPT-5.5 MRCR v2: Long-Context Winner

Published: June 12, 2026

Claude Fable 5 1M Context vs GPT-5.5 MRCR v2: Long-Context Winner

Both Claude Fable 5 and GPT-5.5 ship 1M-token context windows. Anthropic released Fable 5 on June 9, 2026. OpenAI released GPT-5.5 on April 24, 2026. But “1M context” means different things in practice. Here is the honest long-context breakdown.

Last verified: June 12, 2026

TL;DR

Capability	Claude Fable 5	GPT-5.5
Context window	1M tokens	1M tokens
Max output	128K tokens	~16K typical
MRCR v2 (512K–1M)	Not public at this range	74.0% ✅
GraphWalks (long context)	Leads at 512K–1M ✅	Behind
SWE-Bench Pro	80.3% ✅	58.6%
Pricing (per 1M tokens)	$15 in / $75 out	$5 in / $15 out
Best for	Reason over long context	Retrieve from long context

What “1M context” actually means in 2026

Both models accept 1M tokens. That part is real. What differs:

Retrieval quality at the upper range. Can the model actually find a specific fact embedded in 800K of context? That is what MRCR v2 measures.
Reasoning quality at the upper range. Can the model build a chain of inference across 800K of context? That is what GraphWalks measures.
Output budget. Claude Fable 5 supports 128K output; GPT-5.5 caps lower in practice. For long-form generation, Claude wins.

Where each one wins

GPT-5.5 — long-context retrieval (74.0% MRCR v2)

OpenAI’s MRCR v2 benchmark tests multi-round coreference resolution at 512K–1M tokens. Results:

Model	MRCR v2 at 512K–1M
GPT-5.5	74.0%
GPT-5.4	36.6%
Claude Opus 4.7 (prior)	Lower than GPT-5.5 at this range

GPT-5.5 specifically targeted this regression. It is the model to use when:

You do RAG over million-token corpora.
You ask “find this fact in this large document.”
You need a model that genuinely uses the upper end of its context.

For the broader pricing context, see Claude Fable 5 vs GPT-5.5 vs Gemini 3.5 Pro SWE-Bench.

Claude Fable 5 — long-context reasoning (GraphWalks lead)

At the same 512K–1M range, Claude Fable 5 leads on GraphWalks, a benchmark for multi-step reasoning across a long context graph. Anthropic positions Fable 5 as built for “long-horizon agentic tasks” — which in practice means reasoning over and acting on long contexts, not just retrieving from them.

Use Fable 5 when:

You reason across an entire codebase.
You build a multi-step agent that needs to plan over long context.
You need 128K output (e.g., long-form refactor, full documentation regeneration).

Pricing reality at 1M tokens

A single 1M-token query is expensive on either model:

Model	1M input	100K output	Total per query
GPT-5.5	$5.00	$1.50	$6.50
Claude Fable 5	$15.00	$7.50	$22.50

GPT-5.5 is roughly 3.5x cheaper per 1M-token query. The trade-off is reasoning depth.

For RAG workflows at scale, this difference compounds fast. If you run 10,000 1M-token queries per month:

GPT-5.5: $65,000/month
Claude Fable 5: $225,000/month

That cost gap is real and often decisive.

Decision matrix

Workload	Pick
RAG over 800K-token documents	GPT-5.5
Reason across 800K-token codebase	Claude Fable 5
Long-form generation (>16K output)	Claude Fable 5 (128K out)
Find-the-fact in long context	GPT-5.5 (MRCR v2 74%)
Multi-step agent over long context	Claude Fable 5
Cost-sensitive million-token RAG	GPT-5.5
1.5M+ tokens in one shot	Neither — use Gemini 3.5 Pro (2M)
Coding agent on long codebases	Claude Fable 5 (80.3% SWE-Bench Pro)

A realistic routing strategy

For most production systems, the right answer is to route:

Retrieval calls → GPT-5.5 (cheaper, MRCR v2 leader).
Reasoning calls over long context → Claude Fable 5.
Massive context (>1M) → Gemini 3.5 Pro.

Cursor Auto router and the Vercel AI Gateway both expose this kind of multi-model routing. If you build your own, the cost-per-call delta justifies the engineering.

For agent-mode routing, see Cursor 4 SDK vs Claude Code SDK vs Anthropic Agent SDK.

What about Gemini 3.5 Pro at 2M?

Gemini 3.5 Pro extends the context window to 2M tokens — the largest of the three frontier models. As of June 12, 2026, it is rolling out GA after the Google I/O 2026 announcement. Independent benchmarks at 1.5M–2M are not yet available.

For the comparison, see Gemini 3.5 Pro Deep Think vs Claude Fable 5 extended thinking.

Caveats and notes

GPT-5.5’s MRCR v2 number is OpenAI-published. It is OpenAI’s own benchmark. Independent reproduction is consistent but the lab-published bias caveat applies.
Claude Fable 5 was released June 9, 2026. Independent long-context benchmarks at 1M have only a few days of data. Numbers may shift as more tests publish.
GPT-5.6 is expected June 2026 with leaks of a 1.5M context window. If that lands, this comparison rebalances. See GPT-5.6 leaked features.

Bottom line

GPT-5.5 for million-token retrieval (74% MRCR v2, 3.5x cheaper). Claude Fable 5 for million-token reasoning and long-form output (128K output, SWE-Bench Pro 80.3%). For 1.5M+ tokens, use Gemini 3.5 Pro. Production systems should route by task type.

Sources: OpenAI release notes (April 24, 2026), Anthropic news (June 9, 2026), DataCamp, EdenAI, BenchLM benchmark coverage.