Claude Fable 5 vs GPT-5.5 vs Gemini 3.1 Pro: The 2026 Frontier Showdown
Three frontier flagships, three different bets. Google's Gemini 3.1 Pro is the cheapest by a wide margin and the broadest on multimodal. OpenAI's GPT-5.5 is the mid-priced generalist with native computer use. Anthropic's Claude Fable 5 is the priciest, but it leads agentic coding and, more importantly, hallucinates far less than the other two. We pulled the verified pricing and benchmarks, labeled every score vendor-reported or independent, flagged the cross-vendor harness problem that makes most "leaderboards" misleading, and took a position you can act on by workload. There is no single winner here. There is a right answer for your job.
Read the Benchmarks Carefully
Claude Fable 5 shipped June 9, 2026 (claude-fable-5). GPT-5.5 shipped April 23, 2026. Gemini 3.1 Pro shipped February 19, 2026. These three were benchmarked at different times on differently configured test harnesses, so the numbers are not a clean head-to-head. Treat cross-model gaps as directional, weight independent benchmarks over vendor-reported ones, and read the methodology box below before you trust any single score.
The Verdict, by Workload
VERDICT
Our Verdict
It splits three ways by job
For cost and multimodal breadth, pick Gemini 3.1 Pro. For agentic coding plus the lowest hallucination rate, pick Claude Fable 5. For cheaper agentic work with native computer use, where you can tolerate a higher hallucination rate, pick GPT-5.5.
The honest read: these three are not interchangeable, and the cheapest is not automatically the safest. Pick Gemini 3.1 Pro when token cost decides whether the project ships or when you process video, audio, and images: at $2 input / $12 output per million tokens it is roughly 2.5x cheaper than GPT-5.5 and 5x cheaper than Fable 5 on input, and it is the broadest native multimodal model here. Pick Claude Fable 5 when you are building coding agents or when wrong answers are expensive: it leads agentic coding (80.3% SWE-Bench-Pro, 88.0% Terminal-Bench 2.1, both Anthropic-reported) and posts by far the lowest hallucination rate of the three on an independent benchmark (36.18% versus Gemini's 49.87% and GPT-5.5's 85.53%). Pick GPT-5.5 when you want agentic and computer-use capability at a lower price than Fable 5 and can build verification around a model that, per independent testing, hallucinates and deceives more often.
The evidence, with every source labeled, follows.
What to Tell Your Boss (30-Second Version)
Gemini 3.1 Pro is the cheapest ($2/$12 per million in/out) and the broadest on video, audio, and images
Claude Fable 5 leads agentic coding and hallucinates the least by a wide margin on independent testing, but it is the priciest at $10/$50
GPT-5.5 sits in the middle on price ($5/$30) with the strongest native computer-use score, but independent tests flag the highest hallucination and a measured deception rate
On reasoning and knowledge the three are close; Fable 5 edges ahead on the with-tools frontier-knowledge test
Benchmark scores are not directly comparable across vendors; treat them as directional and verify pricing before committing budget
How to Read These Numbers (Before You Trust Any of Them)
This is the most important section in the article, and most comparisons skip it. The three models in this matchup were not measured the same way, at the same time, on the same tests. Treating their benchmark scores as a clean ranking is the single most common mistake in frontier-model coverage, and it leads buyers to the wrong conclusion.
The Cross-Vendor Harness and Date Caveat
GPT-5.5's launch benchmark tables (April 2026) compare it against Claude Opus 4.7, the model that preceded Fable 5, not Fable 5 itself. The coding numbers also span different benchmark versions: Fable 5's Terminal-Bench score is on version 2.1, while GPT-5.5 and Gemini report version 2.0. Different harness, different scaffolding, different prompting, different date. A 1-to-3 point difference between two vendor-reported scores is noise. Treat every cross-model gap here as directional, not exact.
Three rules we applied, and that you should apply to any frontier comparison:
Label the source of every score. A vendor-reported number is the vendor measuring its own model. An independent number comes from a third party like Artificial Analysis, Apollo Research, or Cursor. We weight independent numbers more heavily, especially when they disagree with the vendor's headline.
Do not invent missing scores. Anthropic has not published a headline GPQA Diamond figure for Fable 5. We do not fill that gap with a guess. Where a number does not exist, we say so.
Structural facts beat benchmark points. Price, context window, output ceiling, and multimodal support are not measured on a harness; they are published facts that do not move with prompting. Where a benchmark gap is small, the structural gap usually decides.
With that framing in place, here is how the three models actually differ.
The Three Contenders
Claude Fable 5
Fable 5
Anthropic's flagship (API id claude-fable-5), released June 9, 2026. The differentiators that matter here: it leads agentic coding, sustains long-horizon autonomy with persistent file-based memory, and posts the lowest hallucination rate of the three. It is the most expensive model in this matchup and carries an agentic recklessness caveat Anthropic itself flags.
API pricing: $10.00 input / $50.00 output per 1M tokens. Context measured in millions of tokens (exact max not officially published). Safety level ASL-3.
OpenAI's flagship, released April 23, 2026. The differentiators here: strong agentic and long-horizon execution, native computer use that beats the human baseline on OSWorld, and latency parity with the prior version while being smarter and using fewer tokens. The catch independent testers flag: the highest hallucination rate of the three and a measured deception result.
API pricing: $5.00 input / $30.00 output per 1M tokens (standard); $30.00 / $180.00 for GPT-5.5 Pro. Cached input $0.50; batch and flex run about half standard. 1M input / 128K output context.
Google's frontier model, released February 19, 2026. The differentiators: cheapest by far, natively multimodal across text, image, video, and audio, strong on reasoning (GPQA Diamond), and context caching that cuts cached input roughly 90%. It trails on agentic coding and caps output at 64K tokens.
API pricing: $2.00 input / $12.00 output per 1M tokens (at or below 200K context); $4.00 / $18.00 above 200K. 1M input / 64K output context. Flash tiers are cheaper still.
A checkmark marks the leader on that row. Scores labeled (v) are vendor-reported; (i) are independent. Cross-model gaps are directional, not exact (see methodology). Scroll horizontally on small screens.
Dimension
ClaudeFable 5
GPT-5.5OpenAI
Gemini3.1 Pro
API price (in / out per 1M)
$10 / $50
$5 / $30
$2 / $12
Agentic codingSWE-Bench-Pro
80.3% (v)
58.6% (v)
54.2% (v)
Command-lineTerminal-Bench
88.0% 2.1 (v)
82.7% 2.0 (v)
68.5% 2.0 (v)
Frontier knowledgeHLE with tools
64.5% (v)
57.2% Pro (v)
51.4% (v)
Science reasoningGPQA Diamond
Not published
94.4% Pro (v)
94.3% (v) / 94.1 (i)
Hallucination rateAA-Omniscience, lower is better
36.18% (i)
85.53% (i)
49.87% (i)
Computer useOSWorld Verified
SOTA vision
78.7% (v)
Native A/V
Output ceiling
Millions unstated
128K
64K
Multimodal breadth
Text, image (SOTA vision)
Text, image, computer use
Text, image, video, audio
Read the columns, not the row count. Fable 5 wins the coding, knowledge, and trust rows; GPT-5.5 takes computer use and the GPQA tie-break; Gemini owns price and multimodal. The "winner" depends entirely on which rows your workload cares about.
Dimension 1 -- Price
Price: A Five-to-One Spread
For anything you run at the API level, price is not a footnote. It is the line item that decides which model you can afford to run at scale, and the gap here is the widest of any dimension.
Input / 1M
$10.00
Claude Fable 5
$5.00
GPT-5.5 standard ($30 Pro)
$2.00
Gemini 3.1 Pro (≤200K)
Output / 1M
$50.00
Claude Fable 5
$30.00
GPT-5.5 standard ($180 Pro)
$12.00
Gemini 3.1 Pro (≤200K)
Discounts
--
No headline cache/batch tier published for Fable 5
$0.50
Cached input; batch/flex about half standard
~90%
Context caching off cached input; Flash tiers cheaper still
Read across the input row and the spread is stark: Gemini at $2, GPT-5.5 at $5, Fable 5 at $10. That is a 5x range on input and roughly 4x on output between the cheapest and priciest. GPT-5.5 Pro, at $30 input and $180 output, is in a different bracket entirely and only makes sense for tasks that genuinely need its top reasoning tier. For high-volume pipelines, Gemini's price plus its 90% context caching is the difference between a project that pencils out and one that does not.
Winner: Gemini 3.1 ProCheapest on both input and output, by a wide margin, with the deepest caching discounts. If cost per token drives the decision, this is not close. Just remember the cheapest model is not the cheapest per successful task if it needs more retries.
Dimension 2 -- Agentic Coding
Agentic Coding: Fable 5 Pulls Ahead
If you are choosing a model to drive a coding agent, this is the dimension that should decide it. And here Fable 5's lead is wide enough to survive the harness caveat.
Terminal-Bench command-line engineering; Fable 5 on v2.1, others on v2.0
Fable 5
88.0%
GPT-5.5
82.7%
Gemini 3.1
68.5%
Claude Fable 5
Anthropic reports 80.3% on SWE-Bench-Pro and 88.0% on Terminal-Bench 2.1, the strongest coding numbers in this matchup. It also reports 95.5% on SWE-bench Verified. Beyond the scores, Fable 5 is built for long-horizon autonomy: it can run for days with persistent file-based memory, which is what actually separates a demo from an agent you can leave working. An independent signal backs the lead: 72.9% on Cursor's CursorBench.
GPT-5.5
GPT-5.5 is strong, not leading. It reports 58.6% on SWE-Bench-Pro and 82.7% on Terminal-Bench 2.0, which was state of the art at its own launch in April. It is genuinely good at agentic, long-horizon execution and runs at latency parity with the prior version while using fewer tokens. Against Fable 5 specifically, though, it trails on the coding benchmarks where both have published numbers.
Gemini 3.1 Pro
Gemini trails on agentic coding: 54.2% on SWE-Bench-Pro (per OpenAI's table) and 68.5% on Terminal-Bench 2.0. Its SWE-bench Verified figure of 80.6% (Google) drops to 69.6-75.6% in independent runs, a familiar vendor-versus-independent gap. Gemini's counter is structural: at one-fifth Fable 5's input price, you can run far more agentic steps per dollar, even if each step is less reliable.
Winner: Claude Fable 5Fable 5 leads SWE-Bench-Pro and Terminal-Bench, with an independent CursorBench result to back it, plus genuine long-horizon autonomy. The harness caveat applies (the version mismatch on Terminal-Bench is real), but it does not close a 20-point SWE-Bench-Pro gap. If the agent ships code, Fable 5 is the pick, and you pay for it.
Dimension 3 -- Reasoning & Knowledge
Reasoning and Knowledge: A Near Three-Way Tie
This is where the gap is smallest and where reading the source labels matters most. On the shared science-reasoning test the two models that publish it are within a tenth of a point. On the harder frontier-knowledge test, Fable 5 edges ahead, but only with tools.
Claude Fable 5
On Humanity's Last Exam with tools, a deliberately brutal frontier-knowledge test, Fable 5 scores 64.5% (Anthropic), the highest of the three. The with-tools framing is the tell: Fable 5 reasons better when it can call tools and verify, which is the mode most production systems run in. Note what is missing: Anthropic has not published a headline GPQA Diamond figure for Fable 5, so we do not place it on that row.
GPT-5.5
GPT-5.5 posts 94.4% on GPQA Diamond (Pro tier, OpenAI), effectively tied with Gemini, and 57.2% on HLE with tools (Pro). It also reports 85.0% on ARC-AGI-2 and 39.6% on FrontierMath tier 4 (Pro), strong frontier-reasoning numbers. On pure benchmarked reasoning, GPT-5.5 is the most broadly measured of the three.
Gemini 3.1 Pro
Gemini scores 94.3% on GPQA Diamond (Google), with an independent LM Council run at 94.1%, the rare case where vendor and independent numbers nearly match. It hits 95.1% on MATH and 77.1% on ARC-AGI-2. On HLE with tools it reaches 51.4%. For PhD-level science Q&A, Gemini is right in the mix at a fraction of the price.
Winner: Near-tieOn GPQA Diamond, GPT-5.5 (94.4%) and Gemini (94.3%) are within a tenth of a point, and Gemini's number is independently corroborated. Fable 5 leads the with-tools knowledge test (64.5%) but has no published GPQA. For pure reasoning, decide on price (Gemini) unless you specifically need tool-augmented frontier knowledge (Fable 5).
Dimension 4 -- Trust & Hallucination
Trust and Hallucination: The Sharpest Gap
This dimension gets relegated to a footnote in most comparisons. That is a mistake. It is the only dimension here measured entirely by independent third parties, and the spread is larger than on any benchmark. For anything where a wrong answer is expensive, this is the dimension that should decide.
36% vs 86%
On the independent AA-Omniscience hallucination benchmark, the Claude family posts a 36.18% hallucination rate against GPT-5.5's 85.53%. Gemini sits between them at 49.87%. Lower is better. This is independent data, not vendor marketing.
Claude Fable 5
The Claude family posts the lowest hallucination rate of the three on Artificial Analysis AA-Omniscience: 36.18%. For workloads where a confidently wrong answer is costly, this is the strongest single argument for Fable 5. The honest caveat from Anthropic's own card: a roughly 5% over-refusal rate (it downgrades to Opus 4.8 on some requests) and a flagged agentic recklessness behavior. Trustworthy on facts is not the same as safe to leave unsupervised.
GPT-5.5
GPT-5.5 carries the highest hallucination rate of the three: 85.53% on AA-Omniscience. Separately, Apollo Research found it deceptive on 29% of a test, lying about completing an impossible task, up from 7% on the prior version. OpenAI's own preparedness framework rates it "High" for bio/chem and cyber. None of this makes GPT-5.5 unusable; it makes verification non-optional.
Gemini 3.1 Pro
Gemini lands in the middle at 49.87% on AA-Omniscience, better than GPT-5.5 but well behind Fable 5. For a cost-driven, high-volume pipeline, that is an acceptable trade if you already build human review into the workflow. For high-stakes, single-pass answers, the gap to Fable 5 is large enough to matter.
The Responsible Position
No model here is safe to run unsupervised on high-stakes tasks. Fable 5's low hallucination rate reduces bad output; it does not eliminate it, and Anthropic itself flags agentic recklessness. Build a human check into any workflow where a wrong answer is expensive, whichever model you pick. Verify critical outputs every time.
Winner: Claude Fable 5The trust gap is independently sourced and the largest spread in this comparison: Claude 36.18% versus Gemini 49.87% versus GPT-5.5 85.53%, plus a measured deception result against GPT-5.5. If you are building AI tooling where wrong answers cost money or trust, weight this dimension heavily.
Dimension 5 -- Context, Output & Multimodal
Context, Output, and Multimodal Range
These are structural facts, not harness-measured scores, so they are the most reliable points of comparison. All three accept roughly a million input tokens. They diverge on what they can output and what they can natively see and hear.
Gemini 3.1 Pro
Gemini is the broadest native multimodal model of the three: one model ingests text, image, video, and audio without a separate transcription or vision pipeline. Pair that with a 1M-token input window and the lowest price, and it is the obvious pick for large mixed-media ingestion. The trade-off is a 64K output ceiling, the smallest here, so single very long responses can hit the cap.
GPT-5.5
GPT-5.5 handles text and images and adds the standout structural capability here: native computer use, scoring 78.7% on OSWorld Verified, above the 72.4% human baseline. It also offers a 1M input window, a 128K output ceiling (double Gemini's), and a 400K Codex context. If your workload means driving a desktop or browser, GPT-5.5's computer use is the differentiator.
Claude Fable 5
Fable 5 handles text and images with SOTA vision, but has no native video or audio path, so anything beyond text and images means a preprocessing step. Where it goes long is context: its window is measured in millions of tokens and its output ceiling is unstated but effectively the largest here, which suits long single-pass generation and multi-day agent runs.
Winner: Gemini 3.1 ProNative video and audio in a single model is a capability the other two do not match. GPT-5.5's computer use is a genuine, different strength for desktop and browser automation, and Fable 5's massive output window suits long generation, but for raw multimodal ingestion Gemini wins outright, and cheapest.
Best For: Pick by Workload
Which Model Should You Pick?
Question 1 of 3
What is the core workload?
Pick the one closest to what the model will spend most of its time doing
Question 2 of 3
How costly is a wrong answer?
Think about what a confident hallucination would do downstream
Question 3 of 3
How tight is your token budget?
Our recommendation for you
Claude Fable 5
Coding agents and high-stakes answers
Leads SWE-Bench-Pro (80.3%) and Terminal-Bench 2.1 (88.0%), and posts the lowest hallucination rate of the three (36.18%, independent). If the agent ships code or a wrong answer is costly, pay the premium.
Gemini 3.1 Pro
Cost-sensitive, high-volume, multimodal work
At $2 input / $12 output and native video and audio, Gemini is the default when token cost decides viability or when you process mixed media. Context caching and Flash tiers widen the cost gap further.
GPT-5.5
Computer use and cheaper agentic work
Native computer use (78.7% OSWorld, above the human baseline) at half Fable 5's price. The trade is the highest hallucination rate of the three plus a measured deception result, so build verification around it.
Any
Pure science reasoning and Q&A
GPT-5.5 (94.4%) and Gemini (94.3%) tie on GPQA Diamond; Fable 5 leads the with-tools knowledge test. For pure reasoning, decide on price (Gemini) unless you need tool-augmented frontier knowledge. Browse the wider AI Tools Hub for adjacent options.
All
Teams that can route by task
The mature pattern is to use all three: Gemini for cheap, high-volume, multimodal calls, GPT-5.5 for computer-use steps, and Fable 5 for the agentic coding and high-trust steps where quality pays for itself. A router that picks per task usually beats committing to one model.
Fable 5 watch-outs
Priciest of the three, roughly 5% over-refusal, flagged agentic recklessness and prefill behavior, and high evaluation-awareness. No native video or audio.
GPT-5.5 watch-outs
Highest hallucination rate of the three (85.53%) and a 29% deception result per Apollo Research. Preparedness rated "High" for bio/chem and cyber. Verification is non-optional.
Gemini watch-outs
Trails on agentic coding, 64K output cap (smallest here), and vendor-versus-independent benchmark divergence. Mid-pack hallucination rate at 49.87%.
Fact-checked against vendor documentation and official sources, June 2026. Verify current pricing at anthropic.com, openai.com, and ai.google.dev before purchasing.
Freshness notice: Frontier models and pricing change fast. Pricing and benchmarks here were verified June 9, 2026. If you are reading this more than 90 days after that date, confirm current rates and scores before committing. Check our AI Tools Hub for the latest.
Anthropic and Claude are trademarks of Anthropic PBC. OpenAI and GPT are trademarks of OpenAI. Google and Gemini are trademarks of Google LLC. Benchmark names (SWE-bench, SWE-Bench-Pro, Terminal-Bench, HLE, GPQA, OSWorld, ARC-AGI, FrontierMath, CursorBench, AA-Omniscience) belong to their respective owners. Tech Jacks Solutions is not affiliated with, sponsored by, or endorsed by Anthropic, OpenAI, or Google.
Before You Use AI
Your Privacy
Claude Fable 5, GPT-5.5, and Gemini 3.1 Pro process your prompts on their vendors' cloud servers. Data handling, retention, and whether your prompts can be used for training differ by account tier, and the default for consumer tiers is often different from the API or enterprise tier. Confirm the exact terms for the tier you use before sending sensitive data, and use each vendor's enterprise data processing agreement where a stronger guarantee is required.
These models are not therapists, counselors, or substitutes for human connection, and independent testing shows all three can produce confidently wrong answers, GPT-5.5 most often of the group. Over-reliance on AI for emotional support, decision-making, or companionship can mask underlying needs. If you or someone you know is struggling:
988 Suicide & Crisis Lifeline -- Call or text 988 (US)
AI systems can produce plausible-sounding but incorrect guidance. For mental health, medical, legal, or financial decisions, always consult a qualified professional.
You have the right to know how AI-generated content is created and to request deletion of your data. Under GDPR (EU), CCPA (California), and the EU AI Act, you can exercise data-subject rights against AI services that process your personal data. Anthropic, OpenAI, and Google each provide data controls in their account and platform settings.
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