On June 9, 2026, Anthropic released Claude Fable 5, the most capable model it has ever made generally available, and published a benchmark table putting it head to head against GPT-5.5, Gemini 3.1 Pro, and its own Claude Opus 4.8. The headline is clear: Fable 5 leads the public board on the work businesses actually do. The fine print is just as important, because some of the most eye-catching numbers belong to a restricted model you cannot buy.

This comparison cuts through the launch noise. We line up the benchmarks that matter for real deployments, read the asterisks honestly, compare pricing on a per-task basis, and give you a task-by-task framework for choosing between Fable 5, GPT-5.5, Gemini 3.1 Pro, and Opus 4.8. No model wins every row, and the right answer for most teams is routing, not standardizing.

If you want the full background on Fable 5 itself, the safety split, and the rollout timeline, start with our Claude Fable 5 developer guide.

1The Four Contenders at a Glance

Before the benchmarks, it helps to know what each model is positioned for and what it costs to run.

2The Full Benchmark Matrix (Read the Asterisks)

Anthropic published a comparison across Claude Fable 5 / Mythos 5, Claude Opus 4.8, GPT-5.5, and Gemini 3.1 Pro. Two methodology points are load-bearing. First, the table shows the higher of the Fable 5 and Mythos 5 scores, which are within one to three points of each other on most rows. Second, starred (*) rows are where the two diverge more, because Fable 5's blocking safeguards for cybersecurity and biology pull its score down toward Opus 4.8. On those rows, the number you see is Mythos 5, the restricted model, not the Fable 5 you can deploy.

Source: Anthropic Claude Fable 5 and Mythos 5 benchmark table, June 9, 2026. Starred (*) rows show Mythos 5, the restricted model; Fable 5 performs closer to Opus 4.8 on those because of blocking safeguards. A dash means no comparable published figure.

3Agentic Coding: Where the Gap Is Widest

Coding is where Fable 5 separates from the field most cleanly. On SWE-Bench Pro it scores 80.3%, an 11-point lead over Opus 4.8 (69.2%) and more than 20 points ahead of GPT-5.5 (58.6%) and Gemini 3.1 Pro (54.2%). On the harder FrontierCode Diamond set the relative gap is even larger: 29.3% versus 13.4% for Opus 4.8 and 5.7% for GPT-5.5.

The one row where Fable 5 does not lead outright is Terminal-Bench 2.1, where GPT-5.5 through its own Codex CLI harness posts 83.4%, fractionally ahead of Opus 4.8's 82.7%. Fable 5's 88.0% on that benchmark is starred, so read it as the restricted tier's ceiling. Even so, the practical takeaway holds: for raw, multi-file, long-horizon coding capability, Fable 5 is the strongest model you can deploy today.

4Knowledge Work, Vision, and Tool Use

The pattern from coding repeats across the knowledge-work rows. On GDPval-AA, an ELO-style measure of professional knowledge tasks, Fable 5 posts 1932 against 1890 for Opus 4.8, 1769 for GPT-5.5, and 1314 for Gemini 3.1 Pro. On document vision without tools (GDP.pdf) it leads at 29.8%, with GPT-5.5 second at 24.9%. Tool use (AutomationBench) and legal tasks follow the same shape: a clear Fable 5 lead, Opus 4.8 close behind, and the other two trailing.

Two rows are worth singling out. On spatial reasoning (Blueprint-Bench 2), GPT-5.5 is genuinely competitive at 36.2% against Fable 5's 38.6%, far ahead of Opus 4.8's 14.5%, so if your workload is spatial or diagram-heavy, GPT-5.5 deserves a real evaluation. And on computer use (OSWorld-Verified), the four models cluster tightly between 76% and 85%, with Fable 5 only narrowly ahead.

The throughline: Fable 5's advantage is largest on hard, multi-step, autonomous work and smallest on tasks where all frontier models have converged. That distinction is exactly what should drive your routing strategy and your budget.

5Pricing: The Premium-Tier Decision

Fable 5 lists at $10 per million input tokens and $50 per million output tokens, exactly double Opus 4.8's $5/$25 and the most expensive rate card of the four. The other three are materially cheaper. Here is the full standard rate card, with cached-input rates and the long-context tier that kicks in above 200K prompt tokens for the OpenAI and Google models.

Standard short-context (≤200K prompt tokens) list prices as of June 2026. Long-context column is input / cached / output for prompts above 200K tokens. Fable 5 and Opus 4.8 from Anthropic; GPT-5.5 from OpenAI's pricing page; Gemini 3.1 Pro from Google AI Studio / Vertex AI. Cached-input rates reflect each vendor's 90% cache-read discount. OpenAI and Google also offer a 50% Batch API discount.

To make the rate card concrete, here is what a single agentic task that consumes 200,000 input tokens and produces 50,000 output tokens costs on each model at standard rates (no cache hits):

The formula is cost = input/1,000,000 * P_in + output/1,000,000 * P_out. At Fable 5 rates that is 0.2 * 10 + 0.05 * 50 = $4.50; GPT-5.5 is 0.2 * 5 + 0.05 * 30 = $2.50; Opus 4.8 is 0.2 * 5 + 0.05 * 25 = $2.25; and Gemini 3.1 Pro is 0.2 * 2 + 0.05 * 12 = $1.00. On this representative split, Gemini 3.1 Pro is roughly 4.5x cheaper than Fable 5, and GPT-5.5 and Opus 4.8 both land near half the Fable 5 cost. At one million such tasks a month, that is about $4.5M on Fable 5 versus $1M on Gemini 3.1 Pro, before any caching.

Caching changes the picture for agents that reuse a large system prompt or codebase across many turns. All four vendors price cache reads at roughly one tenth of base input. With a cache hit, Fable 5's input on the example drops from $2.00 to $0.20, GPT-5.5 from $1.00 to $0.10, and Gemini 3.1 Pro from $0.40 to $0.04. Note that Anthropic charges a cache-write premium (1.25x for the 5-minute cache, 2x for the 1-hour cache) on the first call, while OpenAI's standard rows do not add a separate cache-write fee, so high-reuse workloads tilt the math further toward the cheaper models.

6Which Model Should You Use?

No single model wins everything, so route by task instead of standardizing on one. Here is a practical decision guide:

The disciplined approach: run a representative sample of your real tasks on each candidate, measure quality and token spend, and route by task type. If your workload lives near cybersecurity or biology, test Fable 5 specifically, because its safeguards may hand those queries to Opus 4.8 and you could be paying the premium for a fallback answer.

7Why Lushbinary for Multi-Model Builds

Picking a model is the easy part. The hard part is the architecture around it: routing each request to the right model by difficulty, capping agentic spend, exploiting prompt caching, and handling fallbacks gracefully. Lushbinary has shipped production Claude, GPT, and Gemini integrations across healthcare, fintech, SaaS, and e-commerce.

• Model routing and evals - LLM gateways that send each task to the cheapest model that meets your quality bar, backed by an eval harness that proves it.
• Cost control - prompt-cache strategy, budgets, and hard caps so agentic workloads do not surprise you.
• Agent architecture - tool-calling, self-verification loops, and multi-step orchestration tuned to each model's strengths.
• AWS infrastructure - production deployment with VPC isolation, encryption, monitoring, and autoscaling.

8Frequently Asked Questions

📚 Sources

• Anthropic - Claude Fable 5 and Claude Mythos 5 (benchmark table)
• Anthropic - Claude Opus 4.8 (pricing and context baseline)
• CNBC - Anthropic releases Mythos-like AI model to the public
• The Verge - Anthropic releases its first Mythos-class model
• OpenAI - API pricing (GPT-5.5 $5/$30 standard, $10/$45 long context)
• Google - Vertex AI generative AI pricing (Gemini 3.1 Pro $2/$12, $4/$18 above 200K)

Content was rephrased for compliance with licensing restrictions. Benchmark figures and methodology notes sourced from the official Anthropic Claude Fable 5 and Mythos 5 announcement and reporting by CNBC and The Verge as of June 9, 2026. GPT-5.5 and Gemini 3.1 Pro per-token rates gathered from OpenAI's and Google's pricing pages as of June 2026 (standard short-context tier). All pricing may change - always verify on the vendor's official pricing page before modeling cost.