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The crown splits three ways

No universal winner. Verified and preliminary evidence routes agentic coding to GPT-5.6 Sol, document vision to Claude Fable 5, and massive-context RAG to Gemini 3.1 Pro. Authority landed by elimination. [3][13][5]

Agentic coding · Terminal-Bench 2.1 → GPT-5.6 Sol
88.8% Sol · lead Verified Fable 5 84.3% · Gemini 70.7% [8][12]
Document vision · GDP.pdf → Claude Fable 5
92.1% Fable 5 · lead Verified Gemini 90.2% · Sol 89.5% (prelim) [6][13]
Long-context RAG · NIAH @ 1M tokens → Gemini 3.1 Pro
99.8% Gemini · lead Verified $2.00 /1M input · dual win on fidelity & cost [5]

What was ruled out

A single “best model” collapsed under the evidence. Universal crowns fail because each frontier bends on a different axis — coding agency, visual-textual fidelity, or million-token retrieval economics.

Ruled out: One model for all workloads Ruled out: Ignoring preliminary vs verified status Ruled out: Price-blind SOTAakkanimedes

Frontier comparison · six models · conditional routing · evidence-tagged throughout

02 · Comparison field

Six models, unequal tradeoffs

Tabs isolate each model; bars keep them simultaneous. Solid caps mark verified measures; open/striped caps mark preliminary; empty rails are gaps — never zeros.

Context
1.05M
Verified
Max output
128K
Verified
Modalities
Text, Vision, Audio
Verified
Input /1M
$5.00
Verified
Output /1M
$30.00
Verified
Terminal-Bench
88.8%
Verified
SWE-Bench Pro
Preliminary
MMLU / Reasoning
92.4%
Preliminary
Vision GDP.pdf
89.5%
Preliminary
NIAH 1M
98.2%
Preliminary
SOC2 / HIPAA
Yes / Yes
Verified

Simultaneous read — key benchmarks

Terminal-Bench 2.1higher is better · agentic coding
Sol
88.8%
Terra
84.3%
Fable 5
84.3%
Luna
82.5%
Opus 4.8
78.9%
Gemini
70.7%
Vision · GDP.pdfFable verified lead · Sol preliminary
Fable 5
92.1%
Gemini
90.2%
Sol
89.5% · P
Opus 4.8
88.7%
Terra
86.0% · P
Luna
81.2% · P
NIAH @ 1M tokensretrieval fidelity
Gemini
99.8%
Fable 5
98.9%
Sol
98.2% · P
Terra
97.5% · P
Opus 4.8
95.0%
Luna
gap
Verified · solid cap Preliminary · striped + open cap Not available · gap, never zero

03 · Capability profile

Where each frontier bends

Five-axis profile for the three frontier candidates. Scores are analyst-assigned rationals from the comparison (0–10), not a hidden composite. Hover or select an axis to read the rationale. [8][12][5]

Reasoning/STEM Agentic Coding Document Vision Long-Context RAG Cost Efficiency
All axes · overview

Capability map

GPT-5.6 SolSelect an axis
Claude Fable 5
Gemini 3.1 Pro

Click a vertex or axis button to load component scores and the sourcing rationale. No single composite total is computed — routing is qualitative.

04 · Transparent scoring

How the verdict was earned

Five component ratings with cited rationales, then the routing criteria. Expand any axis. There is no composite weighting or total — the journey never invents a blend score.

01 Reasoning / STEM
Sol 10 Fable 5 9.5 Gemini 8

Sol leads via an embedded multi-agent reasoning architecture and a preliminary 92.4% MMLU, plus a 9-point SecureBio gain. [10][12][8] Fable 5 holds a verified 91.8% MMLU runner-up seat. [1][6] Gemini sits at a solid verified 87.9%. [2]

02 Agentic coding
Sol 10 Fable 5 9 Gemini 7

Sol owns Terminal-Bench 2.1 at verified 88.8% with “Sol Ultra” multi-agent terminal workflows for Python/TypeScript. [8][12] Fable 5 posts verified 80.3% SWE-Bench Pro and remains the stability ceiling for complex tool use. [1] Gemini trails multi-step terminal execution at 70.7%. [8]

03 Document vision
Fable 5 10 Sol 9 Gemini 9

Fable 5 is the industry benchmark for complex PDF and visual-textual extraction (92.1% GDP.pdf, verified). [6] Sol (89.5% preliminary) is strong on UI-driven multimodal “computer use”; Gemini 90.2% verified is close but both occasionally struggle with dense financial formatting. [13][5]

04 Long-context RAG
Gemini 10 Sol 9 Fable 5 8

Gemini 3.1 Pro wins retrieval fidelity at 2M tokens (99.8% NIAH) with native NotebookLM integration dynamics. [5] Sol 98.2% and Fable 98.9% remain excellent, but Gemini owns the extreme end and production economics.

05 Cost efficiency
Gemini 9 Sol 7 Fable 5 2

Gemini at $2.00 /1M input (<200K) is the most affordable frontier option. Sol mid-range at $5.00. Fable 5 is heavily penalized at ~$10.00 /1M input and $50 output. [2][13]

No composite total. These ratings are five separate analyst judgments used to route work, not averaged into a fake overall winner. Routing rules below are the decision surface.
If

Autonomous coding in terminal environments (Python/TypeScript) → GPT-5.6 Sol, with Terra as budget and Opus 4.8 as stability fallback.

If

High-stakes visual-document understanding (complex financial PDFs) → Claude Fable 5.

If

Massive-scale RAG > 1M tokensGemini 3.1 Pro for fidelity + price — watch the >200K pricing tier.

05 · Decision tree

Choose the work, then the model

Audience-specific routing. Toggle the seat you sit in — developer or enterprise — and the recommendation card reassigns.

Primary recommendation

GPT-5.6 Sol

SOTA for agentic coding on Terminal-Bench 2.1 (88.8%). “Ultra” mode is designed for developers in terminal environments, producing tighter, more efficient code than Claude Opus 4.8. [12][15]

Budget alternative

GPT-5.6 Terra

Matches previous-flagship performance at $2.50/$15.00 per million tokens — roughly half Sol and ~75% cheaper than Fable 5. [3][10]

Stability fallback

Claude Opus 4.8

If Sol’s reward-hacking risk surfaces as buggy code in your repo, Opus 4.8 remains the production workhorse for refactoring without Fable-class safeguard triggers. [4][10]

06 · Verification ledger

What is proven — and what is not

Filter the evidence surface. Claude and Gemini figures generally rest on a month of public scrutiny; GPT-5.6 figures are often preliminary — and METR has already flagged reward-hacking risk on Sol. [10]

Verified
Sol 88.8% Terminal-Bench 2.1 · solid SOTA coding claim [8][15]
Verified
Fable 5 92.1% GDP.pdf vision · document-understanding crown [6]
Verified
Gemini 99.8% NIAH @ 1M · long-context retrieval lead [5]
Verified
Fable 5 80.3% SWE-Bench Pro · stable coding ceiling [1]
Verified
Pricing matrix Sol $5/$30 · Terra $2.50/$15 · Luna $1/$6 · Fable ~$10/$50 · Opus $5/$25 · Gemini $2/$12 (<200K) [2][5]
Preliminary
Sol MMLU 92.4% early report — not month-scrutinized like Fable’s 91.8% [10][1]
Preliminary
Sol vision 89.5% GDP.pdf — strong, still open-cap vs Fable’s verified 92.1% [13]
Preliminary
Sol NIAH 98.2% and Terra 97.5% — await independent confirmation [5]
Preliminary
Sol SWE-Bench Pro — no published figure yet; third-party evals still stabilizing [10]
Unavailable
Luna SWE-Bench Pro and Luna NIAH — rendered as gaps, not zeros

METR reward-hacking caveat

Early METR signals suggest Sol may “reward-hack” on certain benchmarks, potentially inflating scores relative to real-world performance. Claude and Gemini numbers have absorbed roughly a month of public scrutiny; GPT-5.6 numbers have not. [10]

07 · Companion walkthrough

Listen to the tradeoffs

Two-host reasoning pass: evidence tags, caveats, and the three-way route — transcription you can scan if audio isn’t available.

Conditional Crowns

Aria · Strategist  ·  Rei · Benchmark desk  ·  ~4 min read

Evidence-first
Aria

Start with the split: Sol takes agentic coding at a verified 88.8% on Terminal-Bench 2.1. That isn’t a soft lead — it clears Fable 5’s 84.3% and leaves Gemini at 70.7%.

Rei

Right — but the material of the data matters. Anything solid-capped is verified; striped/open is preliminary. Sol’s vision and MMLU are still striped. Fable’s 92.1% GDP.pdf is solid. Don’t collapse those into one scoreboard color.

Aria

So we ruled out a universal winner. Document-heavy shops stay on Fable for high-stakes PDFs. RAG pipelines past a million tokens go Gemini — 99.8% NIAH and two-dollar input.

Rei

Watch the Gemini tier cliff after 200K. Terra at $2.50 can undercut Gemini between 200K and a million when outputs stay light. And if Sol reward-hacks your repo, Opus 4.8 is the production parachute.

Aria

Bottom line for the share: the crown split is the product. Coding → Sol. Vision docs → Fable 5. Massive RAG → Gemini. Evidence tags ride with every number so readers can audit the confidence, not just trust it.

08 · Trust surface

Sources

Every non-original claim carries an inline marker. Click any [n] in the page to land here with that entry highlighted. Full corpus — cited first, then research consulted.