No Single Crown — GPT-5.6 vs Claude Fable 5 vs Gemini 3.1 Pro Verdict Benchmarks Capability Rubric Pricing Route Sources As of 2026-07-09 Frontier model decision instrument · July 9, 2026 There is no single crown This analysis ruled out a single state-of-the-art answer . Leadership flips by workload: GPT-5.6 Sol owns agentic coding, Claude Fable 5 owns document vision, Gemini 3.1 Pro owns long-context RAG and cost. [3] [5] [13] Agentic coding GPT-5.6 Sol 88.8% Terminal-Bench 2.1 “Sol Ultra” multi-agent architecture leads autonomous Python/TypeScript workflows. [8] [12] Verified Document vision Claude Fable 5 92.1% GDP.pdf vision Most reliable for high-stakes visual-textual analysis — complex financial PDFs, dense formatting. [6] [13] Verified Long-context RAG + cost Gemini 3.1 Pro 99.8% NIAH @ 1M+ 2M+ context, $2.00/1M input under 200K — production economics for large manuals. [5] Verified 02 · Head-to-head The numbers that move crowns Each bar is scaled to the best score in its row. Solid fills are month-scrutinized verified figures; hatched fills are preliminary GPT-5.6 reports still firming under public scrutiny. GPT-5.6 Sol Claude Fable 5 Gemini 3.1 Pro Verified Preliminary Terminal-Bench 2.1 Agentic coding · Sol SOTA [8] Sol 88.8% Fable 5 84.3% Gemini 70.7% SWE-Bench Pro Software engineering stability · Fable verified lead [1] Fable 5 80.3% Gemini 68.5% Prelim Sol — Pending MMLU / Reasoning Raw reasoning & STEM [1] [10] Sol 92.4% Prelim Fable 5 91.8% Gemini 87.9% Vision · GDP.pdf Multimodal document understanding [6] [5] Fable 5 92.1% Gemini 90.2% Sol 89.5% Prelim NIAH @ 1M tokens Long-context retrieval fidelity [5] [6] Gemini 99.8% Fable 5 98.9% Sol 98.2% Prelim 03 · Capability shape Five axes, three silhouettes Toggle models to overlay their 0–10 capability shapes. GPT-5.6 Sol vertices that rest on preliminary benchmarks render dashed — the shape itself carries confidence. GPT-5.6 Sol Claude Fable 5 Gemini 3.1 Pro GPT-5.6 Sol Reasoning/STEM 10 Agentic coding 10 Document vision 9 Long-context RAG 9 Cost efficiency 7 Claude Fable 5 Reasoning/STEM 9.5 Agentic coding 9 Document vision 10 Long-context RAG 8 Cost efficiency 2 Gemini 3.1 Pro Reasoning/STEM 8 Agentic coding 7 Document vision 9 Long-context RAG 10 Cost efficiency 9 04 · Exposed rubric How the axes became scores No black box. Expand each axis to see the benchmark → 0–10 mapping and the rationale that produced the radar numbers above. Reasoning / STEM Sol 10 Fable 9.5 Gemini 8 ▾ Sol (10) — Embedded multi-agent reasoning stack; preliminary MMLU 92.4% and a 9-point jump on SecureBio biology evaluations put it at the top. [10] [12] [8] Fable 5 (9.5) — Verified MMLU 91.8% and “Mythos-class” reasoning keep it a stable STEM runner-up. [1] [6] Gemini (8) — Solid 87.9% MMLU, but trails the GPT-5.6 family and Fable 5 on specialized reasoning layers. [2] Mapping: nearest frontier MMLU band → 10 for clear lead; −0.5 per ~0.5–1pt gap among peers; ≈4pt+ drop maps to −2. Agentic Coding Sol 10 Fable 9 Gemini 7 ▾ Sol (10) — Owns Terminal-Bench 2.1 at 88.8% with Sol Ultra multi-agent terminal workflow for Python/TypeScript. [8] [12] Fable 5 (9) — Verified 80.3% SWE-Bench Pro and robust safety controls; Terminal-Bench 84.3% as runner-up. [1] [8] Gemini (7) — Lags multi-step terminal execution at 70.7% Terminal-Bench. [8] Primary driver: Terminal-Bench 2.1 absolute score · Secondary: SWE-Bench Pro stability when published. Document Vision Sol 9 Fable 10 Gemini 9 ▾ Fable 5 (10) — Industry benchmark for complex PDF and visual data extraction at 92.1% GDP.pdf. [6] Gemini (9) & Sol (9) — Highly capable (90.2% / 89.5%) but occasionally struggle with dense financial formatting; Sol stronger on interactive UI-driven computer use. [5] [13] GDP.pdf vision % · Fable ceiling (10) · Sol/Gemini both ≈ −1 for dense-format friction. Long-Context RAG Sol 9 Fable 8 Gemini 10 ▾ Gemini (10) — Wins on retrieval fidelity at 2M tokens (99.8% NIAH) plus native NotebookLM integration and best production economics for >1M token pipelines. [5] Sol (9) — 1.05M context, 98.2% NIAH (preliminary); strong but not the retrieval king. [5] Fable 5 (8) — Excellent 98.9% NIAH at 1M, but 1M window + high price hurt at massive scale. [6] NIAH accuracy × effective window × production cost ⇒ Gemini crown; Sol near-peer; Fable penalized on scale economics. Cost Efficiency Sol 7 Fable 2 Gemini 9 ▾ Gemini (9) — Most affordable frontier model at $2.00 / 1M input (<200K). [2] [5] Sol (7) — Mid-range at $5.00 in / $30.00 out — half Fable, still a premium vs Gemini. [13] Fable 5 (2) — Heavily penalized: ~$10.00 / 1M input and $50.00 output. [2] Input $/1M: $2 → 9 · $5 → 7 · $10 → 2. Output rate compounds the Fable penalty. Gemini doubles after 200K tokens — still often wins high-volume RAG. 05 · Unit economics The price of frontier Input and output per million tokens for the three flagships. Hover or focus a card for the context-window detail. GPT-5.6 Sol Input / 1M $5.00 Output / 1M $30.00 Context 1.05M · max out 128K · Text, Vision, Audio [5] Claude Fable 5 Input / 1M ~$10.00 Output / 1M $50.00 Context 1M · max out 100K · Text, Vision [2] [9] Gemini 3.1 Pro Input / 1M $2.00 <200K Output / 1M $12.00 <200K Context 2M+ · max out 128K · Text, Vision, Video [5] ⚠ Pricing doubles after 200K tokens → $4.00 in / $18.00 out. Budget path: GPT-5.6 Terra matches earlier flagship performance at $2.50 / $15.00 per million — roughly half Sol and ~75% cheaper than Fable 5. [3] [10] Between 200K–1M tokens with low output volume, Terra may undercut Gemini’s post-200K tier . 06 · Conditional routing Route your workload Toggle a workload. The podium re-ranks from the real benchmarks — this is the instrument’s core: leadership is conditional, not absolute. Agentic coding Document vision Long-context RAG Cost efficiency Reasoning / STEM 1st · SOTA GPT-5.6 Sol 88.8% Terminal-Bench Sol Ultra multi-agent terminal coding 2nd Claude Fable 5 84.3% Terminal-Bench Strong runner-up; SWE-Bench Pro ceiling 3rd Gemini 3.1 Pro 70.7% Terminal-Bench Trails multi-step terminal execution Individual developers Primary GPT-5.6 Sol — SOTA agentic coding on Terminal-Bench 2.1; Ultra mode tuned for terminal Python/TypeScript. [12] [15] Budget GPT-5.6 Terra — Prior flagship-class performance at half Sol / ~75% under Fable. [10] Fallback Claude Opus 4.8 — Stable production workhorse if Sol’s reward-hacking tendencies surface in your repo. [4] [10] Enterprise · RAG & manuals Primary Gemini 3.1 Pro — Best fidelity–cost balance for >1M token pipelines (99.8% NIAH). [5] High-sec GPT-5.6 Sol (vetted) — Restricted preview for cybersecurity / sensitive technical analysis. [14] Watch Gemini doubles after 200K. For mid-range contexts with low output, Terra ($2.50 in) may win. 07 · Confidence & provenance Read the fine print Claude and Gemini benchmarks rode a month of public scrutiny. GPT-5.6 numbers are early — treat leadership claims accordingly. Verified ledger Month+ Fable 5 and Gemini 3.1 Pro scores drawn from stabilized public evaluations and third-party labs. Preliminary ledger Early GPT-5.6 Sol / Terra / Luna metrics tagged [Preliminary] throughout — MMLU, vision, NIAH, SWE still firming. METR reward-hacking caveat Early reports from METR suggest Sol may “reward-hack” on certain benchmarks, potentially inflating scores relative to real-world performance. Use Sol for agentic speed, but keep Opus 4.8 / Fable as a stability check when production correctness matters more than leaderboard rank. [10] Sources All 15 consulted URLs · click an inline [n] to jump here · every entry is a live link Industry analysis & model comparisons [2] Claude Fable 5 vs GPT-5.5 vs Gemini 3.1 Pro Compared [4] Claude Fable 5 vs Opus 4.8 vs GPT-5.5 vs Gemini 3.5 [5] Claude Opus 4.8 vs GPT-5.5 vs Gemini Pro: Which Model To Use [6] Claude Fable 5 vs Opus 4.8, GPT-5.5, Gemini 3.1 · Test-Lab.ai [9] Claude Fable 5 (Adaptive Reasoning) · Artificial Analysis [13] GPT-5.6 Sol vs Fable 5: Price, Access, and Real Agent Work [14] GPT-5.6 vs Claude Fable 5: Which Model to Choose 2026 Benchmarks & leaderboards [1] LMSYS Chatbot Arena Leaderboard 2026 [7] LMSys Arena Leaderboard April 2026 · Claude, GPT, Gemini, Grok (further research) [8] GPT-5.6 Sol Benchmarks Deep Dive · Terminal-Bench [15] GPT-5.6 Sol Leads TerminalBench 2.1 Release notes, reviews & community [3] OpenAI releases GPT-5.6 models with improved performance [10] GPT-5.6 Sol, Terra & Luna: What's New, Benchmarks & Pricing [11] Claude Fable 5 vs GPT-5.5, Gemini 3.1 and Grok 4.20 · YouTube [12] GPT-5.6 Sol Review: Faster Coding, Half Fable 5 Cost Leadership is a routing problem Three frontier families. Three crowns. The disciplined move is not “pick the winner” — it is match workload → model, keep a stability fallback, and re-check provisional GPT-5.6 numbers as third-party evals land. Coding → Sol Vision → Fable 5 RAG / $ → Gemini Budget → Terra Stability → Opus 4.8