GPT-6 Astra vs Claude Fable 5.1
Same $10/$50 headline price. Compare long-context billing, cache economics, reasoning, coding, agents and independent benchmark evidence.
1.05M vs 1M context · 128K output eachCompare models ↗
AI MODEL COMPARISONS
Compare AI models.
By workload, not hype.
Decision-oriented comparisons of GPT-6, Claude and Gemini models using official specifications, normalized cost examples and clearly labeled benchmark evidence.
Start with the question your application actually needs to answer: capability, coding, agents, long context, multimodal input, cost or platform fit.
5deep comparisons
3major providers
1M+context class covered
2026-09-26hub verified
START HERE
High-value model decisions.
GPT-6 Astra vs Claude Fable 5.1
Same $10/$50 headline price. Compare long-context billing, cache economics, reasoning, coding, agents and independent benchmark evidence.
1.05M vs 1M context · 128K output eachOpen comparison ↗
GPT-6 Sol vs Gemini 3.8 Flash
Compare lower-cost production models on token economics, 1M+ context, tools, reasoning controls and broad multimodal input.
$2/$10 vs $0.75/$3.75* · 1M+ contextOpen comparison ↗
COMPARISON LIBRARY
Find the matchup that answers your question.
Every card below is a direct, crawlable link. Search and filters only change what you see; they do not create separate thin URLs or hide the underlying comparison pages from navigation.
5published matchups
GPT-6 Sol vs Gemini 3.8 Flash
Compare lower-cost production models on token economics, 1M+ context, tools, reasoning controls and broad multimodal input.
$2/$10 vs $0.75/$3.75* · 1M+ contextCompare models ↗
Claude Opus 5.5 vs Gemini 3.8 Flash
Compare long-running coding, cache economics, Batch and Fast inference, multimodal inputs, tools and deployment fit.
1M+ context · cache + batch analysisCompare models ↗
GPT-6 Sol vs Claude Opus 5.5
A direct comparison of two serious coding and agent models, including standard and long-context costs, reasoning controls and tool architecture.
$2/$10 vs $4/$20 · 1.05M vs 1MCompare models ↗
GPT-6 Astra vs Sol vs Luna
Choose the right GPT-6 tier by capability, workload and unit economics instead of treating GPT-6 as one model.
Same 1.05M context · 100× price spreadCompare models ↗
No comparison matches that filter. Try a provider or workload term.
COMPARE BY QUESTION
Start from the decision, not the brand.
Which top-end model fits hardest work?
Start with Astra vs Fable when you care about frontier reasoning, long-horizon agents, cache economics and independent benchmark evidence.
GPT-6 Astra vs Claude Fable 5.1 ↗Which model should run complex coding loops?
Compare Sol vs Opus for OpenAI/Anthropic agent architecture, then add Gemini when multimodality or lower token economics are central.
Which model changes the production bill?
Use the pages with normalized workloads, caching and long-context examples—not only headline token rates.
Which model handles richer media input?
Gemini 3.8 Flash directly accepts text, images, video, audio and PDFs, making its direct matchups useful for media-heavy workflows.
Which GPT-6 tier should you route to?
Compare Astra, Sol and Luna before reaching across providers. The same family spans a 100× price range at listed short-context rates.
GPT-6 Astra vs Sol vs Luna ↗SXF COMPARISON METHOD
What makes a useful AI model comparison.
Google's own people-first guidance asks whether a page adds substantial value beyond obvious summaries. SXF comparisons are built around the parts that change a real deployment decision: source quality, normalized economics, architecture and uncertainty.
Official facts first
Context windows, output limits, pricing, modalities, reasoning controls and product availability come from vendor documentation whenever possible.
Normalize the economics
We calculate representative workloads and call out cache reads/writes, Batch or Fast tiers, long-context multipliers and temporary promotional pricing.
Separate evidence types
Vendor benchmark claims are labeled as vendor claims. Independent benchmark data is presented separately with its reasoning settings and methodological caveats.
No universal winner
Model choice depends on task distribution, tools, latency, permissions, cost targets and acceptance criteria. The pages map tradeoffs instead of manufacturing a single score.
HOW TO READ MODEL COMPARISONS
Five variables usually matter more than the benchmark headline.
01Task acceptance
Does the output actually pass your test, review or business criterion?
02Total task cost
Include reasoning tokens, retries, cache behavior, tools and human correction—not only list price.
03Agent architecture
Tool calling, browser/computer use, async work, state persistence and steering can matter as much as the base model.
04Context behavior
Nominal window size is only capacity. Retrieval, compaction, cache reuse and long-context pricing determine whether that capacity is useful.
05Evaluation configuration
Reasoning effort, tools, scaffolding and benchmark version can materially change measured performance.
MODEL REFERENCES
Verify the models before comparing them.
DEEP FAMILY GUIDE
GPT-6 vs Claude in 2026.
Need the full family-level view instead of one model pair? Compare Astra, Sol and Luna with Fable 5.1, Opus 5.5, Sonnet 5 and Haiku 4.5 across pricing, context, coding and agent architecture.
COMPARISON STANDARD
SXF methodology and editorial approach↗ Model Intelligence database↗ Expert AI guides↗ Latest primary-source signals↗COMPARE FAQ
What does SXF compare between AI models?
SXF compares official API pricing, cached-input economics, context and output limits, reasoning controls, modalities, tool support, agent workflows, deployment constraints and workload fit. Independent benchmark evidence is added when a useful comparable source is available.
Does SXF choose one best AI model?
No. Model selection is workload-specific. A model can be stronger for one benchmark or workflow and weaker for another, while pricing, latency, tools and context economics can change the practical decision.
How current are the model comparisons?
The hub and comparison pages show a verification date and are generated from SXF's maintained model intelligence layer. Pricing and availability can change quickly, so the comparison pages link directly to vendor documentation used for verification.
How are AI model prices compared?
Token prices are normalized per million input and output tokens where possible. SXF also uses concrete workload examples and calls out long-context multipliers, caching, Batch or Fast tiers and other charges that can make headline rates misleading.
Are benchmark scores directly comparable across vendors?
Not always. Reasoning effort, prompts, tools, scaffolding, fallback behavior and benchmark versions can differ. SXF distinguishes vendor-reported results from independent evidence and recommends reproducing representative tasks in your own evaluation harness.
Which models are covered?
The current comparison library centers on the models with the strongest SXF reference pages and decision value: GPT-6 Astra, Sol and Luna, Claude Fable 5.1 and Opus 5.5, and Gemini 3.8 Flash. Coverage expands selectively rather than adding thin comparison pages.