Claude Fable 5 vs Opus 4.8 vs GPT-5.5: SWE-Bench, Pricing, When to Switch

TL;DR — Anthropic shipped Claude Fable 5 on June 9, 2026, its first publicly available Mythos-class model. It hits 95.0% on SWE-bench Verified and 80.3% on SWE-bench Pro — an 11-point lead over Opus 4.8 and 21.7 points clear of GPT-5.5. Pricing is $10/$50 per million tokens, exactly 2x Opus 4.8. GPT-5.5 still wins Terminal-Bench 2.1 (82.7% vs 80.5%), Opus 4.8 still owns long-context retrieval and price-performance, and the upgrade math turns on whether your bottleneck is capability or bill. Below: the real numbers, the cost-per-point math, and a decision tree you can apply today.

Fable 5 is the first publicly available model to clear 80% on SWE-bench Pro and 95% on Verified — but at $10/$50 per million tokens, the cost per SWE-bench point runs 72% higher than Opus 4.8.

What Each Model Actually Shipped

Three releases over seven weeks reset the top of the coding leaderboards.

GPT-5.5 dropped on April 23, 2026 as OpenAI’s single flagship — no Standard/Pro split for capability, just two surfaces (GPT-5.5 and GPT-5.5 Pro) for cost and latency. The launch leaned on Codex CLI and computer use; “agentic coding” was the headline. GPT-5.5 Instant followed on May 5 as the default model in ChatGPT.

Claude Opus 4.8 landed on May 28, 2026 at the same $5/$25 price as 4.7. SWE-bench Pro jumped from 64.3% to 69.2%, OSWorld-Verified to 83.4%, and Artificial Analysis’s independent GDPval-AA leaderboard put it 121 Elo points clear of GPT-5.5 on real economic work — using 35% fewer output tokens per task than 4.7. Same price, higher score, lower bill. We covered the full release in our Opus 4.8 review.

Claude Fable 5 shipped on June 9, 2026 — yesterday, as of this writing. It’s Anthropic’s first generally available model from the Mythos class, the family Anthropic previously held back because of cybersecurity capabilities Anthropic deemed too risky for broad release. Fable 5 is the Mythos model with three safety classifiers layered on top: when a query hits cybersecurity, biology/chemistry, or distillation patterns, the request automatically routes to Opus 4.8 instead. Pricing is $10/$50 — half of what Anthropic charged for Mythos Preview, but still 2x Opus 4.8.

The headline isn’t that Anthropic shipped two models in two weeks. It’s that the gap between capability leader and value leader widened — and they’re now both Claude.

The SWE-Bench Picture, Side by Side

Coding benchmarks are noisy. SWE-bench Verified and SWE-bench Pro are the two that matter most for production decisions because they run against real GitHub issues end-to-end, with a maintainer-graded ground truth. Here’s how the three line up:

Three things in that table are worth more than the headline numbers.

Every’s Senior Engineer benchmark is the cleanest read on capability ceiling. Every runs it on the hardest coding problems they can write — the kind a senior engineer would take a working day to solve. Fable 5 at 91/100 lands in the range of the human engineers who’ve taken the test. Opus 4.8 at 63 and GPT-5.5 at 62 are essentially tied, and both sit in the “junior engineer with debugger” range. The 28-point gap between Fable 5 and Opus 4.8 on this test is the gap that justifies the price premium — if your work lives at that ceiling.

Terminal-Bench is the one place GPT-5.5 still wins, and the asterisk matters. GPT-5.5 hits 82.7% against Fable 5’s 80.5% — close, but a real lead. The asterisk: GPT-5.5’s score comes through Codex CLI, OpenAI’s strongest agentic surface for terminal work. Fable 5’s number is the model in a standard harness. On Codex CLI, GPT-5.5 has had two months to embed itself in real workflows; if your stack is already Codex-centric, “switch to Fable” isn’t a free upgrade. We unpack this trade-off in Codex CLI configuration.

Long-context retrieval is a Claude-family lead that compounded. On the GraphWalks BFS benchmark at 1M tokens, Opus 4.8 hits 68.1% versus GPT-5.5’s 45.4% — a 22.7-point spread that turns into “the agent actually remembers what happened on turn 12” in practice. Anthropic hasn’t published Fable 5’s GraphWalks score directly, but the long-context architecture is shared, so the gap to GPT-5.5 on million-token retrieval almost certainly persists.

Pricing, and What “Cost Per Benchmark Point” Actually Buys

Sticker pricing is straightforward. The interesting number is what each model returns per dollar.

Blended assumes a 2:1 input-to-output token ratio typical of coding workloads (more context in than code out). ofox.ai routing applies the same per-token rates with no markup.

Cost per SWE-bench Pro point is the metric most teams should care about, because it’s what your monthly invoice looks like when you scale agentic coding traffic. Fable 5’s $0.62 is 72% more expensive per point than Opus 4.8’s $0.36. GPT-5.5 sits between at $0.50 — losing on absolute capability to both Claudes, but cheaper per point than Fable 5.

Two adjustments push the math in Fable 5’s favor before you write it off as a luxury:

Fable 5 finishes the same task in fewer turns. Anthropic’s reported numbers, corroborated by independent runs, put Fable 5 at roughly 25–30% fewer turns than Opus 4.8 on agentic spreadsheet and codebase tasks. If your bottleneck is output token volume — common on long autonomous runs — that efficiency partially offsets the 2x rate card. Opus 4.8 already runs 35% fewer output tokens than 4.7; Fable 5 pushes that further.

The capability ceiling is real on the hardest 10–20%. If your team’s escalation pattern today is “Opus 4.8 hands off to a human after three failed attempts,” routing those handoffs to Fable 5 instead may finish the task without the human in the loop. The cost question stops being “which model is cheaper per token” and becomes “which model removes a senior engineer from the loop.” That comparison usually pays out at Fable 5’s price.

Test the routing math on your own workload. Through ofox.ai, one key gets you Opus 4.8 and GPT-5.5 today (Fable 5 rolling in), on a single OpenAI-compatible endpoint. Run the same prompts through all three, compare token counts and quality on your workload before committing to the upgrade.

When to Switch: A Decision Tree

The right question isn’t “which model wins” — Fable 5 wins most benchmarks. The right question is “which model wins on my task and bill.” Here is the routing logic that maps the published numbers to a defensible choice.

1. Your primary workload is long-horizon agentic coding (multi-hour runs, codebase-wide migrations). Use Fable 5. The Senior Engineer benchmark, the FrontierCode Diamond lead, and the 25–30% turn reduction all compound on long runs. The price premium is offset by fewer wasted turns and fewer human escalations. Best AI model for coding walks through the routing patterns that work at this scale.

2. Your primary workload is terminal-driven CLI work, ops automation, or you’re already on Codex CLI. Use GPT-5.5. Terminal-Bench 2.1 is the only benchmark of the three GPT-5.5 leads on, and the gap on Codex-centric workflows is real — not benchmark noise. The 7-week head start on integration matters here.

3. Your primary workload is everything else — refactors, code review, daily agent loops at scale. Use Opus 4.8. Same $5/$25 pricing as Opus 4.7, top of the GDPval-AA real-work leaderboard, 35% fewer output tokens than the prior generation. For 80% of teams, this is the right answer in 2026 — and it stays the right answer until your workload pushes past the capability ceiling.

4. You need million-token context retrieval (legal review, codebase audits, long transcripts). Use Opus 4.8 (or Fable 5 if you can absorb the price). GPT-5.5’s 45.4% on GraphWalks BFS at 1M tokens is the disqualifying number — it means the model is no longer reliably finding facts past the first ~200K tokens. The Claude family architecture is the only one that holds up at that scale today.

5. You’re hitting refusals or routing to Opus 4.8 on Fable 5. Expected behavior, not a bug. Fable 5’s three safety classifiers (cybersecurity, biology/chemistry, distillation attempts) trigger on ~5% of sessions per Anthropic, and the fallback is silent — the request runs on Opus 4.8 anyway. If your workload sits in any of those three areas (security research, biotech, model training pipelines), don’t try to engineer around the classifier. Just call Opus 4.8 directly and skip the indirection.

The one routing pattern that doesn’t survive the new numbers: “Opus is the daily driver, GPT-5.5 for math and long context.” That logic was true through May. GraphWalks closed the long-context gap. Opus 4.8 closed the math gap (USAMO 2026 jumped from 69.3% on Opus 4.7 to 96.7% on 4.8). If you’re routing math or long-context to GPT-5.5 today, you’re paying more per output token for a worse result.

How to Access Through ofox.ai

The three models land on a single OpenAI-compatible endpoint, so the upgrade path from “use one model” to “test all three” is one base URL change.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.ofox.ai/v1",
    api_key="your-ofox-key",
)

# Claude Opus 4.8 — daily driver
opus = client.chat.completions.create(
    model="anthropic/claude-opus-4.8",
    messages=[{"role": "user", "content": "Audit this service for race conditions..."}],
)

# GPT-5.5 — terminal-heavy workflows
gpt = client.chat.completions.create(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "Write a shell script that..."}],
)

Opus 4.8 and GPT-5.5 are live on ofox.ai today at anthropic/claude-opus-4.8 and openai/gpt-5.5. Fable 5 is rolling into the aggregator now — check the model page or the changelog for the live ID. One key covers all three, and going through an aggregator makes the capability vs. cost question easier to answer empirically: same prompts, three models, one endpoint, real numbers on your traffic.

For Anthropic-native features (adaptive thinking, effort control on Opus 4.8), point the official Anthropic SDK at https://api.ofox.ai/anthropic instead. We walk through both protocols in Why use an LLM API gateway.

Verdict

Fable 5 is the new capability ceiling. Opus 4.8 is the new value floor. GPT-5.5 is the ecosystem play that still wins one important benchmark.

If you’re shipping agentic coding to production in 2026, the migration path is no longer “pick one and go.” Route Opus 4.8 by default, escalate the hardest 10–20% to Fable 5, and keep GPT-5.5 on Codex CLI workflows where it has the integration lead. The cost-per-point math justifies the routing complexity within the first few thousand requests.

The one thing that hasn’t changed: independent leaderboards still beat vendor claims. Watch Artificial Analysis’s GDPval-AA for Fable 5’s real-work Elo when it lands. That’s the number that will tell you whether the 2x price tag holds up against the 25–30% turn reduction outside the benchmark suite.

Related: Claude Opus 4.8 Release Review — the daily-driver Claude in depth. GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro — the previous-generation matchup. Best AI Model for Coding 2026 — where each model fits the coding landscape. Best AI Model for Agents 2026 — picking models for long autonomous runs.