Quick answer. OpenAI's GPT-5.6 (three tiers — Sol, Terra, Luna) and Anthropic's Claude Fable 5 are the two newest frontier models of mid-2026, and they landed within a month of each other. On Artificial Analysis's independent Intelligence Index (July 2026 snapshot), Fable 5 leads at about 60 and GPT-5.6 Sol follows at about 59 — a near tie. The real split is cost and coding style: Fable 5 costs $10/$50 per million tokens and leads the vendor-reported SWE-Bench Pro single-shot code-correctness test (~80% vs Sol's ~65%), while Sol costs $5/$30, tops the independent agentic Coding Agent Index, and does a comparable task for roughly a third of Fable's price. Fable is the pick when vendor-reported single-shot correctness matters most; Sol is the value-and-agentic pick.
Two of the newest frontier models arrived within weeks of each other in mid-2026: Anthropic shipped Claude Fable 5 on June 9, and OpenAI made GPT-5.6 generally available on July 9. They take opposite approaches. Anthropic put out one premium "Mythos-class" flagship. OpenAI put out a family of three tiers — Sol, Terra, and Luna — so you pick the intelligence-per-dollar point that fits each task.
| At a glance (July 2026) | GPT-5.6 Sol | Claude Fable 5 |
|---|---|---|
| Price in / out (per 1M) | $5 / $30 | $10 / $50 |
| AA Intelligence Index (independent) | ~59 (#2) | ~60 (#1) |
| AA Coding Agent Index (independent) | 80 (#1) | 77 |
| SWE-Bench Pro (vendor-reported) | ~64.6% | ~80.3% |
| Cost per task (AA task set) | ~$1.04 | ~3× Sol's |
| Context window | ~1M | ~1M |
This is a neutral, source-led comparison: where each model genuinely wins, where the benchmarks disagree, and how to choose. All vendor claims are labelled as such; the independent numbers come from Artificial Analysis's public testing, dated to the July 2026 snapshot (absolute index scores drift as the harness is re-versioned, so the relative ordering is the durable signal).
What are we comparing?
GPT-5.6 is not a single model. OpenAI ships three durable tiers under one version number, all reasoning models with vision input and roughly 1M-token context:
- Sol (
gpt-5.6-sol) — the flagship, for the hardest reasoning, long-horizon agents, and coding. Sol also has two heavy compute modes — Sol Pro and Sol Ultra (four cooperating sub-agents) — which run on the same model and price, not as separate tiers. - Terra (
gpt-5.6-terra) — the balanced production tier, pitched as roughly GPT-5.5 quality at half the price. - Luna (
gpt-5.6-luna) — the cheapest, fastest tier for high-volume work.
Full tier breakdown in our GPT-5.6 Sol, Terra & Luna guide.
Claude Fable 5 (claude-fable-5) is Anthropic's first generally available "Mythos-class" model — a tier positioned above the Opus line, which Anthropic calls its most powerful generally available model. It's a single premium model with a 1M-token context and up to 128K output tokens, and it auto-routes a small slice of sensitive queries (cybersecurity, biology/chemistry) to Claude Opus 4.8 for safety. So the honest framing is one premium Anthropic model vs a three-tier OpenAI family — most of this comparison pits Fable 5 against Sol, the closest GPT-5.6 tier.
GPT-5.6 vs Claude Fable 5: pricing
Pricing is where the two strategies diverge hardest. These are the published API list prices per million tokens:
| Model | Input / 1M | Output / 1M | Cached input / 1M |
|---|---|---|---|
| Claude Fable 5 | $10.00 | $50.00 | $1.00 (90% off) |
| GPT-5.6 Sol | $5.00 | $30.00 | $0.50 |
| GPT-5.6 Terra | $2.50 | $15.00 | $0.25 |
| GPT-5.6 Luna | $1.00 | $6.00 | $0.10 |
Fable 5 is the most expensive model in this comparison by a wide margin — its output tokens cost $50/M, which is 1.67× Sol's $30 and over 8× Luna's $6. Anthropic prices Fable as a "use it where it pays for itself" model, not a default. OpenAI's counter-move is the tiering: Terra targets GPT-5.5-class quality at $2.50/$15, and Luna undercuts everyone at $1/$6, so teams can route the cheap turns down the stack and reserve Sol for the hard 10%.
Which is smarter? Intelligence benchmarks
On Artificial Analysis's aggregate Intelligence Index (independent, July 2026 snapshot, models run at maximum reasoning effort), the two sit almost on top of each other, with Fable narrowly ahead:
| Model | AA Intelligence Index | Where it ranks |
|---|---|---|
| Claude Fable 5 | ~60 | #1 overall intelligence |
| GPT-5.6 Sol | ~59 | #2, one point behind Fable |
| GPT-5.6 Terra | ~55 | ≈ GPT-5.5-class |
| GPT-5.6 Luna | ~51 | cheapest frontier-adjacent tier |
A one-point gap on an aggregate index is within noise — The Decoder summarized it as "Sol nearly matches Fable 5 on aggregated benchmarks at one-third the cost." Two independent data points do separate them, though. On ARC-AGI (novel-reasoning puzzles), the ARC Prize team reported GPT-5.6 Sol at 96.5% on ARC-AGI-1 and 92.5% on ARC-AGI-2, and it became the first model to make meaningful progress on the much harder ARC-AGI-3. On AA-Briefcase (realistic multi-step office tasks), Artificial Analysis scored Fable 5 clearly ahead (rubric ~56%, Elo ~1764) versus Sol (rubric ~42%, Elo ~1592). Read together: Sol looks especially strong on hard, self-contained reasoning; Fable is stronger on long, messy, real-world analytical work.
Which is better at coding? (the benchmarks disagree)
This is the crux, and it's the part most single-number comparisons get wrong. The two leading coding benchmarks point in opposite directions, because they measure different things:
| Coding benchmark | GPT-5.6 Sol | Claude Fable 5 | What it measures |
|---|---|---|---|
| AA Coding Agent Index (independent) | 80 (in Codex, #1 overall) | 77 (in Claude Code) | End-to-end agentic coding throughput |
| SWE-Bench Pro (vendor-reported) | ~64.6% | ~80.3% | Raw single-shot patch correctness |
Sol leads the independent agentic harness — driving tools, iterating, and completing multi-step coding work inside Codex, it posts the top Coding Agent Index score Artificial Analysis has measured. Fable leads on vendor-reported single-shot correctness — on SWE-Bench Pro, which grades whether a single generated patch actually resolves the issue, Anthropic reports Fable 5 at roughly 80% versus OpenAI's ~65% for Sol, a ~15-point gap. Neither number is "wrong." If your workload is an autonomous agent grinding through a task list, Sol's throughput and lower cost win. If it's hard, single-shot correctness on gnarly bugs where a wrong patch is expensive, Fable's edge is worth paying for. (Both SWE-Bench Pro figures are vendor-reported; treat them as directional.)
Cost per task: the efficiency story
Per-token price is only half the bill — the other half is how many tokens each model burns to finish a task. Here OpenAI leans on GPT-5.6's efficiency. Artificial Analysis measured GPT-5.6 Sol at roughly $1.04 per task on its Intelligence Index (Terra ~$0.55, Luna ~$0.21), and reported Sol completing a comparable task at about one-third of Claude Fable 5's cost — a product of both the lower per-token price and fewer tokens spent. OpenAI's own headline claim is up to 54% fewer output tokens than GPT-5.5 on agentic coding (a vendor number; Artificial Analysis's independent measurement of general-task token use was more modest). The takeaway is consistent across sources: for the same result, GPT-5.6 is materially cheaper to run than Fable 5 — that's the core of OpenAI's value pitch.
Context, speed, and tool use
On raw specs the two are close: both carry roughly a 1M-token context window and up to 128K output tokens. Where GPT-5.6 adds something new is Programmatic Tool Calling — in OpenAI's Responses API, the model can write and run JavaScript in an isolated, network-less sandbox to orchestrate its own tool calls (loops, conditionals, parallel calls) instead of round-tripping every call through the app. OpenAI cites customer-reported token savings from it (one customer reported ~63% fewer total tokens). GPT-5.6 also adds a new max reasoning-effort rung and persisted reasoning across turns. Fable 5's story is different: Anthropic positions it for long, self-correcting autonomous runs — multi-hour agent work with verification loops — and it offers a 90% prompt-caching discount to soften its premium price. Both are strong tool-users; the difference is Sol exposes more low-level agent plumbing, while Anthropic tunes Fable for hands-off long-horizon autonomy.
Where each one wins — and when to use which
- Choose Claude Fable 5 when correctness on hard, single-shot problems matters more than cost: complex refactors where a wrong patch is expensive, long analytical/office workflows (where it leads Artificial Analysis's AA-Briefcase), and multi-hour autonomous agent runs that compound. You're paying a premium for its narrow lead on the intelligence index and its single-shot correctness edge.
- Choose GPT-5.6 Sol when you want near-Fable intelligence and the best agentic-coding harness at roughly a third of the cost per task — the default value pick for most frontier work, especially inside Codex.
- Choose GPT-5.6 Terra for the bulk of production traffic: GPT-5.5-class quality at $2.50/$15.
- Choose GPT-5.6 Luna for high-volume, latency-sensitive, cheap work — summaries, drafts, classification.
The most cost-effective architecture usually isn't "pick one." It's routing: Luna or Terra for the easy turns, Sol for the hard steps, and Fable 5 reserved for the specific tasks where its correctness edge pays for itself. For head-to-head coding detail, see our GPT-5.6 vs Claude Opus 4.8 coding comparison.
How they fit the wider 2026 field
Neither model exists in a vacuum. As of the July 2026 Artificial Analysis snapshot, the frontier clusters tightly: Claude Fable 5 (~60) and GPT-5.6 Sol (~59) lead, with Google Gemini 3.1 Pro (~57) close behind and cheapest on input ($2/M) with the biggest context (2M tokens), Claude Opus 4.8 (~56) as Anthropic's lower-cost flagship, and xAI/SpaceXAI Grok 4.5 (~54), which is cheapest on output ($2/$6) and tops agentic tool-use benchmarks. See our Grok 4.5 guide for that end of the market. The pattern in 2026 is clear: the intelligence gap between frontier labs has narrowed to a few points, so the real competition has moved to price, token efficiency, and how well each model drives an agent.
FAQ
Is GPT-5.6 or Claude Fable 5 better?
On raw intelligence they're within a point on Artificial Analysis's index (Fable ~60, Sol ~59). Fable 5 wins hardest single-shot code correctness (SWE-Bench Pro ~80% vs ~65%) and long analytical tasks; GPT-5.6 Sol wins the agentic Coding Agent Index and costs roughly a third as much per task. "Better" depends on whether you optimize for peak correctness (Fable) or intelligence-per-dollar and agentic throughput (Sol).
How much cheaper is GPT-5.6 than Claude Fable 5?
Sol is $5/$30 per million tokens versus Fable 5's $10/$50 — half the input price and 40% cheaper on output. Terra ($2.50/$15) and Luna ($1/$6) are cheaper still. On measured cost per task, Artificial Analysis put Sol at about one-third of Fable 5's cost.
What are the GPT-5.6 tiers?
Sol (flagship, $5/$30), Terra (balanced, $2.50/$15), and Luna (cheap/fast, $1/$6). Sol also has heavier compute modes — Sol Pro and Sol Ultra (four cooperating sub-agents) — that run on the same Sol model and price, not as separate tiers.
Which has the bigger context window?
Both GPT-5.6 (all tiers) and Claude Fable 5 offer roughly a 1M-token context window with up to 128K output tokens. Google's Gemini 3.1 Pro is larger at 2M.
Which is better for coding agents?
GPT-5.6 Sol leads Artificial Analysis's Coding Agent Index (80, measured in Codex) and adds Programmatic Tool Calling for agent orchestration, at lower cost. Claude Fable 5 leads on raw single-shot correctness (SWE-Bench Pro) and is built for long, self-correcting autonomous runs. Pick Sol for cost-efficient agentic throughput, Fable for correctness on the hardest tasks.
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