GPT-5.6 Sol vs Claude Fable 5 vs Gemini 3.1 Pro (2026): Which Frontier Model, by Task

What Changed: GPT-5.6 Went GA, and the Frontier Split Three Ways

The headline event is availability. GPT-5.6 first surfaced on June 26, 2026 as a limited preview, open to roughly 20 government-approved partners while a U.S. government access review ran its course. That gated window is what a lot of early coverage froze on. It's out of date now: on July 9, 2026, OpenAI made GPT-5.6 generally available across ChatGPT (Plus and up), Codex, the API, and GitHub Copilot. So Sol isn't a locked preview anymore — it's a shipping product you can buy today. The access story is worth one line of context, not a live caveat.

GPT-5.6 ships in three tiers: Sol at $5 per million input tokens and $30 per million output, Terra at $2.50/$15, and Luna at $1/$6. Sol is the flagship, and it introduced a multi-agent "ultra" mode that runs four agents by default — worth flagging because some of Sol's strongest published numbers come from that mode, not the base model, and the two shouldn't be blurred together.

The more interesting shift is what the field looks like once you line the three frontier options up. A year ago the debate was which single model sat on top. That framing has broken. Fable 5 holds the peak on in-repo coding quality but is the most expensive option and is metered by usage credits. Sol leads agentic and terminal work and does it more cheaply, with a token-efficiency story that matters as much as the per-token rate. Gemini 3.1 Pro trails both on agentic coding benchmarks but wins on price, context, and the simple fact that it's broadly available with no gating history. There is no crown to hand out here — and saying so plainly is the independent read that vendor-adjacent launch recaps tend to skip.

Two pieces of lineup context keep the framing straight. First, Fable 5 is not Mythos 5. In Anthropic's tiers, Mythos 5 sits above Fable as a separate Glasswing / critical-infrastructure-only tier — it's reference-only, not a consumer option, so where a comparison cites a Mythos 5 number (some Terminal-Bench tables list Mythos 5 near 88.0%) that's a different model than the Fable 5 you can actually buy. We keep them separate throughout. Second, Gemini 3.5 Pro is not confirmed. Google has been targeting July for it without a firm public date and declined to comment; a reported mid-July launch that "beats Fable 5 and GPT-5.6" is an unverified leak. We use the verified, generally available Gemini 3.1 Pro as the third model and flag 3.5 Pro only as imminent-but-unconfirmed.

The Benchmarks, Mode-Labeled and Attributed

Benchmark numbers only mean something if you know the exact test, the exact variant, and where the figure came from — so the table labels all three. These are vendor-reported figures from each company's launch materials and system cards; independent replications lag launches, so read them as the makers' claims, mode-labeled, not as settled third-party results.

One distinction to hold before the table: SWE-bench Pro and SWE-bench Verified are different tests with different problem sets, so their scores are not interchangeable. And on July 8, 2026, OpenAI published an audit arguing that about 30% of SWE-bench Pro tasks are flawed — overly strict tests, misleading or incomplete descriptions. That audit is why the SWE-bench Pro row below carries an inline caveat: Fable 5's category-leading result is real, but the benchmark it's measured on is now contested.

Benchmark (variant + mode)	GPT-5.6 Sol	Claude Fable 5	Gemini 3.1 Pro
Terminal-Bench 2.1 (CLI / agentic)	88.8% Sol Ultra multi-agent: 91.9%	— (Mythos 5, separate tier: ~88.0%)	~70.7%
SWE-bench Pro (in-repo coding) — benchmark contested*	~64.6%	~80%*	—
Context window	large	1M tokens	large / long-context
Availability	GA (July 9, 2026)	GA, metered (redeployed July 1)	GA
Standout strength	Agentic / terminal + token efficiency	Peak in-repo coding quality	Value + context + availability

*SWE-bench Pro caveat: on July 8, 2026 OpenAI published an audit finding ~30% of SWE-bench Pro tasks are flawed (overly strict tests, misleading/incomplete descriptions). Fable 5's ~80% is category-leading but should be read with the benchmark's reliability now in question. Sol's ~64.6% is on the same test. A dash means the vendor did not publish a comparable figure on that exact benchmark — we leave it blank rather than guess. The Mythos 5 Terminal-Bench figure is a separate, critical-infrastructure-only tier, not Fable 5.

Read the highlights carefully, because they don't all point the same way. On Terminal-Bench 2.1, GPT-5.6 Sol's 88.8% is the top published number among usable consumer options, and its "ultra" multi-agent mode pushes that to 91.9% — a figure that belongs to the four-agent configuration, not the base model, which is why it's labeled separately. Gemini 3.1 Pro trails meaningfully here at around 70.7%. On SWE-bench Pro, Fable 5's roughly 80% leads the category and sits well above Sol's ~64.6% — but that's exactly the benchmark OpenAI's July 8 audit called into question, so the gap is real but the measuring stick is contested. The honest summary: Sol owns terminal and agentic work, Fable 5 owns in-repo coding quality on a benchmark that's now disputed, and Gemini 3.1 Pro isn't leading either race — its case is made on price and availability, which the next section covers.

Pricing and the Honest Cost Math

Prices are per million tokens, from each vendor's published rates; verify current numbers before budgeting, since introductory windows and metering rules shift.

Model	Input / M	Output / M	Notes
GPT-5.6 Sol	$5	$30	Flagship tier; GA July 9. Terra $2.50/$15, Luna $1/$6
Claude Fable 5	$10	$50	Metered via usage credits; classifier can reroute to Opus 4.8
Gemini 3.1 Pro	~$2	~$12	Lowest headline rate; large context

On headline rate, the order is clear: Gemini 3.1 Pro is cheapest (~$2/$12), Sol sits in the middle ($5/$30), and Fable 5 is the priciest ($10/$50) — roughly double Sol on output and about double Opus 4.8's rate too. But sticker price is only half the coding-cost story. On some coding workflows, Sol has been reported to use significantly fewer output tokens than Fable 5 — often around one-third — and to land at a lower cost per task, because it tends to be terser at reaching an answer. That figure is workflow-dependent and reported rather than a universal constant, so read it as a tendency, not a fixed ratio. Where it holds, Sol's effective coding cost undercuts Fable 5 by more than the per-token gap alone suggests — the efficiency acts as a second discount stacked on the first.

Two things keep the cost comparison honest. First, Fable 5 is metered, not a flat subscription — it's billed through usage credits, and a safety classifier reroutes some flagged requests to Opus 4.8, so the model you pay for isn't always the model that answers. That's a real cost-predictability wrinkle, not just a line item. Second, different tokenizers mean per-token price is not per-task cost. The same text can tokenize to roughly 1.3× more tokens on one model than another, so a lower per-token rate can partly evaporate once the token count is normalized. Compare on estimated cost per task, not the rate card alone — that's the only apples-to-apples way to read these three.

Head-to-Head, by Task

No single model wins across the board, so the useful way to read this is task by task. Here's where each one lands.

Agentic and terminal work

This is GPT-5.6 Sol's category. Its 88.8% on Terminal-Bench 2.1 is the strongest published number among the usable options here, and the "ultra" multi-agent mode (four agents by default) reports 91.9% on top of that. That terminal-benchmark lead is the concrete, measured signal here — we'll let it stand for Sol's agentic strength rather than reach for vaguer capability claims. If your workflow is shell-first — CLI agents, command orchestration, terminal-driven tooling — Sol is the pick, and its token efficiency makes that lead cheaper than the rate card implies.

In-repository coding quality

Claude Fable 5 posts the highest SWE-bench Pro figure at roughly 80%, category-leading for multi-file, repository-level coding quality — the kind of work our Claude Code review covers in depth. Two honest qualifiers travel with that number, though. The benchmark itself is contested after OpenAI's July 8 audit, so don't read the 80% as a settled fact. And Fable 5 is the priciest, metered option, with classifier reroutes in the mix. It's the peak-quality choice when in-repo correctness is what you're buying and you'll pay the premium to get it.

Value and long context

Gemini 3.1 Pro is the value pick. It trails on agentic coding (Terminal-Bench 2.1 around 70.7%), but at roughly $2/$12 it's the cheapest of the three by a wide margin, ships a large/long context window, and is broadly available with none of Sol's gating history. For high-volume, cost-sensitive work, long-document processing, or teams that want a capable frontier model without a metered premium, it's the sensible default. And it's not purely a budget fallback: on long-context synthesis, multimodal input, and retrieval-heavy workloads, Gemini 3.1 Pro competes on the merits — those are tasks that lean on its context window and breadth rather than on topping an agentic-coding leaderboard, which is where it trails.

Cost efficiency

Two models share this one, for different reasons. Gemini 3.1 Pro wins on raw headline price. Sol wins on effective coding cost, because its token efficiency — reported at around one-third of Fable 5's output tokens on some coding workflows — pulls its per-task cost below what $5/$30 suggests. Fable 5 is the outlier on the expensive end: highest rate, metered billing, and a classifier that can change which model actually serves your request. Normalize for tokenizer differences before you commit to a budget.

Availability

All three are generally available today — which itself is new, since Sol spent its first two weeks gated. The nuance is in the history and the terms: Sol opened to everyone on July 9 after a preview-only start, Gemini 3.1 Pro has been broadly available with no gating, and Fable 5 is available but metered rather than freely usable on a flat plan. "Available" isn't quite uniform across the three, and the terms matter as much as the checkmark.

Which to Use: Routing by Use Case

Match your primary workload to a model rather than chasing a single "winner."

Pick this	If your work is…	Why
GPT-5.6 Sol	Terminal/CLI agents, agentic workflows, cost-aware coding	Leads Terminal-Bench 2.1 (88.8%, ultra 91.9%); reported ~⅓ the output tokens of Fable 5 on some coding workflows, at ~half the price; GA since July 9
Claude Fable 5	Peak in-repo coding quality where correctness is worth a premium	Category-leading SWE-bench Pro (~80%, benchmark contested); 1M context — but priciest and metered, with classifier reroutes
Gemini 3.1 Pro	Value, long-context, high-volume, availability-first teams	Cheapest by far (~$2/$12); large/long context; broadly available with no gating history

Pick GPT-5.6 Sol if…

…your agents live in the terminal, your workflow is heavily agentic, or you want frontier coding at a controlled cost. Sol leads the terminal benchmark, its ultra mode pushes that higher, and its token efficiency means the bill lands below where its $5/$30 rate would suggest — often around half of Fable 5's effective coding cost. It's now generally available, so the earlier "preview-locked" reason to skip it is gone.

Pick Claude Fable 5 if…

…in-repository coding quality is the thing you're buying and you'll pay a metered premium for the top of the curve. Fable 5 has the highest SWE-bench Pro number and a 1M-token context window. Go in clear-eyed on two points: that benchmark is contested after the July 8 audit, and Fable 5 is billed by usage credits at $10/$50 with a classifier that can reroute flagged requests to Opus 4.8. For a lot of teams, open-weight cost-leaders or Sol will cover the same work for less — Fable 5 earns its price only when peak quality genuinely justifies it.

Pick Gemini 3.1 Pro if…

…value, context length, and availability outrank winning a benchmark. It's the cheapest of the three by a wide margin, handles long documents well, and is broadly available. It won't top Sol on terminal work or Fable 5 on in-repo quality, but for high-volume or cost-sensitive workloads it's the pragmatic frontier choice. And keep an eye on Gemini 3.5 Pro — it's reportedly close, but as of mid-July it's unconfirmed, so we're not routing work to specs that don't yet exist. For the wider field, our best AI coding agents guide maps the tools these models run inside.

Frequently Asked Questions

Verdict

There's no universal winner here, and pretending otherwise would miss the actual story. The frontier has split three ways, cleanly enough that the honest recommendation is a routing table, not a crown. GPT-5.6 Sol going generally available on July 9 is what makes this a live decision rather than a preview curiosity — it's a shipping product now, it leads terminal and agentic work, and its token efficiency makes that lead cheaper than its rate card looks.

So route by task. Take Sol for agentic and terminal work and cost-aware coding. Take Fable 5 when peak in-repo coding quality is worth a metered premium — going in clear-eyed that its headline SWE-bench Pro number rests on a benchmark OpenAI's July 8 audit put in question, and that its $10/$50 metering with classifier reroutes is a real cost consideration. Take Gemini 3.1 Pro when value, long context, and availability matter more than topping a leaderboard, with Gemini 3.5 Pro a watch-item that isn't confirmed yet. The best model has become three different answers — and knowing which question you're asking is the whole game. For how these models behave inside the tools they run, see our Claude Code review, the Sonnet 5 vs Opus 4.8 vs GPT-5.5 comparison, and the wider AI Coding category.