GPT-6 Astra vs Claude Fable 5.1: Specs, Benchmarks, Pricing, and Model Position
A source-backed comparison of GPT-6 Astra and Claude Fable 5.1 across public specifications, coding, agents, research, safety, cache economics, and current Modelflare access.
OpenAI released GPT-6 Astra on September 3, 2026. OpenAI describes it as its most intelligent and aligned model and the most capable model it has broadly deployed. Two days earlier, Anthropic released Claude Fable 5.1, a frontier model aimed at long-running coding, research, and knowledge work. Both models target difficult reasoning and agent workflows, but they make different trade-offs.
This article answers the practical comparison question: what do GPT-6 Astra and Claude Fable 5.1 actually offer, where is the measurable progress, and which model should a team choose now? The factual cutoff for this review is September 4, 2026, Asia/Shanghai. Provider claims, provider-reported benchmark scores, and the live Modelflare catalog are kept separate.
One important clarification comes first. Neither OpenAI nor Anthropic publishes a verified total-parameter count, active-parameter count, layer count, or training-FLOPs figure for these models on the official pages reviewed here. The “parameters” comparison below therefore means public model specifications, not an invented parameter number.
Executive answer
GPT-6 Astra is the broader end-to-end flagship. It is the better starting point when the work must move through a browser or desktop application, coordinate several tools, edit and test software, or operate under strict authorization and safety boundaries. Its API adds async tool calling, mid-turn steering, and effort changes that preserve the cached prompt prefix.
Claude Fable 5.1 remains a top-tier alternative rather than a model that became obsolete overnight. It is especially attractive for long-horizon coding, research, documents, spreadsheets, and slides. Its official cache-read price is $0.25 per million tokens, one quarter of Fable 5’s rate and lower than GPT-6 Astra’s $1 cache-read rate. For a cache-heavy agent that repeatedly rereads the same context, that difference can matter more than a headline benchmark win.
There is no universal “best” model. A useful rule is:
- choose GPT-6 Astra for broad computer-use, tool orchestration, coding, science, and safety-sensitive agent workflows;
- choose Claude Fable 5.1 for long research and artifact production, cache-heavy sessions, or an existing Claude Messages integration;
- choose neither by name alone when latency, throughput, or cost per successful task is the real constraint—measure the complete workflow, including retries, tool turns, and human review.
Public specifications: what is known and what is not
The numbers in this table come from the OpenAI GPT-6 Astra model page, the OpenAI model guidance, and Anthropic’s Fable 5.1 overview and pricing documentation.
| Public specification | GPT-6 Astra | Claude Fable 5.1 | Practical reading |
|---|---|---|---|
| Provider model ID | gpt-6-astra | claude-fable-5-1 | Use the exact ID; a product nickname is not an API contract. |
| Release date | September 3, 2026 | September 1, 2026 | Both are current-generation releases at this review date. |
| Published parameter count | Not disclosed | Not disclosed | Do not repeat third-party parameter numbers as official facts. |
| Context window | 1,050,000 tokens (1.05M) | 1,000,000 tokens (1M) | Astra is slightly larger on paper; retrieval quality still depends on the task. |
| Maximum output | 128,000 tokens | 128,000 tokens | The output ceiling is effectively the same. |
| Reliable knowledge cutoff | April 30, 2026 | June 2026 | Neither cutoff is a substitute for current retrieval. |
| Input → output | Text and images → text | Text and images → text | Neither model page lists native audio or video output for this comparison. |
| Reasoning control | low, medium, high, xhigh, max | Adaptive thinking, always on; default high | The controls are not interchangeable across providers. |
| Standard input / output | $10 / $50 per MTok | $10 / $50 per MTok | Sticker prices match before cache, batch, or gateway modifiers. |
| Cache read | $1 / MTok | $0.25 / MTok | Fable 5.1 has the lower official cache-hit price. |
| Cache writes | $12.50 / MTok | $12.50 (5-minute) or $20 (1-hour) / MTok | Compare cache lifetime as well as the write rate. |
| Long-context billing | Above 272K input tokens: 2× input/cache and 1.5× output for the full request | Full 1M context at standard per-token pricing | A large context window does not imply identical economics. |
| Provider availability | Trusted Access rollout, followed by API and paid ChatGPT plans | Active latest model for all Claude API customers and partner platforms | Provider availability is separate from Modelflare availability. |
The table also shows why “same $10/$50 price” is an incomplete comparison. GPT-6 Astra offers a larger nominal context window and a rich tool surface, while Fable 5.1 makes repeated cache reads unusually inexpensive and includes its full 1M window at standard pricing. Neither provider has published a parameter figure that can be responsibly compared.
What GPT-6 Astra changes
End-to-end execution
The central GPT-6 Astra upgrade is workflow execution. The model is documented for complex reasoning, software engineering, browsing, computer use, research, and document creation. In the Responses API it supports web search, file search, image generation, code interpreter, hosted shell, apply-patch, computer use, MCP, tool search, streaming, function calling, and structured outputs.
The new API behaviors are designed for tasks that do not fit one request-and-answer turn:
- Async tool calling lets the model continue independent reasoning while an application runs a tool, then resume with the original call ID.
- Mid-turn steering lets a client send a correction while the model is working over a WebSocket continuation, preserving completed work.
- Dynamic effort lets an application raise or lower reasoning effort mid-conversation without rebuilding the cached prompt prefix.
These are meaningful changes for agent developers because they move control from “retry the whole prompt” toward “continue the current task with a bounded update.” The application still owns tool execution, authorization, timeouts, and side effects.
Computer use and professional artifacts
OpenAI reports that Astra reaches 72.6% on its OSWorld 2.0 offline partial score versus 65.7% for GPT-5.6 Sol, with roughly 47% less simulated task time in that comparison. In a Codex harness, OpenAI reports 1.9× faster task completion on Mind2Web after combining the model and harness improvements. These are provider-reported evaluations, not a Modelflare latency guarantee.
The intended output is not only a paragraph of text. Astra is trained to produce usable documents, spreadsheets, presentations, plots, websites, and tested code while following an existing template or style. That makes it a strong candidate when the last mile—checking a UI, updating a file, or validating a result—matters as much as the reasoning in the middle.
Safety and control are part of the release
GPT-6 Astra is the first OpenAI model that the company says reaches the Critical level of cybersecurity capability in its Preparedness Framework. The same release adds stronger prompt-injection resistance in browsing and workplace settings, production misalignment monitoring for tool-using inference, and stricter isolation and monitoring around deployment.
The safety story has an important nuance: capability and monitorability do not move in exactly the same direction. OpenAI reports fewer high-severity misalignment flags and better scope adherence than GPT-5.6 Sol, but also reports that Astra can control its written chain of thought more effectively in adversarial monitor-evasion tests. For production systems, this means model alignment is one layer; authorization, least-privilege tools, confirmation rules, audit logs, and a kill switch remain application responsibilities.
What Claude Fable 5.1 still does differently
Long-horizon work
Anthropic positions Fable 5.1 as the successor to Fable 5 for long-running agentic coding, multistep research, and documents, spreadsheets, and slides. Its full 1M-token context is priced at standard rates, adaptive thinking is always on, and effort can be adjusted without invalidating the prompt cache. Fable 5.1 also improves vision work over dense charts, filings, and PDF tables, plus recovery from failed computer-use steps.
Fable 5.1’s clearest economic difference is cache reuse. Its cache-read price is $0.25 per MTok, while Fable 5 remains at $1 per MTok. Anthropic says the same model is also available as Claude Mythos 5.1 with a more restricted safeguard profile, but Mythos access is limited to trusted programs; it should not be treated as a generally available substitute for Fable 5.1.
API behavior
Fable 5.1 is not a completely transparent model-name swap for a custom Messages client. Anthropic documents three breaking boundaries:
- forced tool use with tool_choice types any or tool returns a 400 error;
- earlier Claude models cannot read Fable 5.1 thinking blocks;
- editing an earlier message, system prompt, or tool list can invalidate later thinking blocks.
The safe migration shape is append-only history, automatic tool choice with strict schemas, and turn-scoped system messages for temporary instructions. The release also adds per-message effort, readable progress updates, and content provenance. In long loops, Fable 5.1 can issue one tool call where Fable 5 batched several, so round-trip count should be measured rather than assumed.
Benchmark comparison
The following selected scores are reproduced from the OpenAI GPT-6 Astra launch comparison, which includes Anthropic’s reported Fable 5.1 results where available. They show the shape of the current frontier, not a universal ranking.
| Evaluation | GPT-6 Astra | Claude Fable 5.1 | What the result suggests |
|---|---|---|---|
| AutomationBench | 41.4% | 31.4% | Astra leads this business-workflow evaluation. |
| BenchCAD | 95.9% | 84.3% | Astra leads the reported CAD/professional task set. |
| Terminal-Bench 4.0 | 57.9% | 55.8% | Both are strong; Astra has a modest reported lead. |
| DeepSWE v1.1 | 74.1% | 67.4% | Astra leads this software-engineering agent evaluation. |
| Terminal-Bench Science 0.1 | 64.6% | 52.6% | Astra’s reported advantage is larger on this science workflow. |
| FrontierMath Tier 4 (v2) | 97.6% | 87.8% | Astra reports a substantial math lead in this setup. |
| GPQA Diamond | 96.0% | 93.7% | Both are near the top; the gap is smaller. |
| Humanity’s Last Exam, with tools | 57.2% | 65.0% | Fable 5.1 leads this reported tool-assisted exam score. |
| HealthBench Professional | 63.4% | 58.1% | Astra leads this reported professional health evaluation. |
| ExploitGym | 42.4% | 30.4% | Astra reports a higher cyber capability score; use safeguards. |
| Artificial Analysis Intelligence Index v4.1.1 | 61.2 | 65.7 | The external index shown in the launch table favors Fable 5.1. |
The exceptions matter. OpenAI’s own table shows Fable 5.1 ahead on Humanity’s Last Exam with tools and on the Artificial Analysis Intelligence Index. Several evaluations use different harnesses, effort settings, tools, task edits, and safety-intervention policies; some entries are missing or not directly comparable. A provider score is evidence about a defined setup, not proof that one model wins a team’s prompts.
Cost and cache economics
At the provider list level, both models charge $10 per million input tokens and $50 per million output tokens. The cost divergence appears in the modifiers:
- GPT-6 Astra cache reads are $1 per MTok; cache writes are $12.50 per MTok. Batch and Flex are listed at 50% of standard rates, while Fast mode is 2× the applicable standard price. Requests with more than 272K input tokens use the documented 2× input/cache and 1.5× output rates for the full request.
- Fable 5.1 cache reads are $0.25 per MTok; 5-minute and 1-hour writes are $12.50 and $20 per MTok. Its full 1M context is priced at standard rates, and Batch is 50% off input and output.
For a simple cache-read comparison, one million cached input tokens cost about $1.00 on GPT-6 Astra versus $0.25 on Fable 5.1 at official list prices. Ten such reads leave a $7.50 difference before output, tool charges, gateway discounts, or retries. This is not a claim that Fable always produces a cheaper completed task: a slower loop, extra tool turns, or more output can erase the saving.
Use a complete-task equation rather than a single token rate:
task cost = input tokens + cache reads + cache writes + output tokens + tool or service charges
Each term must be multiplied by the applicable price, and the result should be divided by successful tasks rather than requests. Tokenizers also differ, so character counts are not a reliable cross-provider budget.
Workload decision matrix
This matrix is the article’s original decision aid. It combines the documented feature surface, the selected benchmark evidence, the pricing mechanics, and the current access boundary; it is not a new benchmark.
| Workload | Starting choice | Why | Boundary to validate |
|---|---|---|---|
| Browser or desktop tasks with several tools | GPT-6 Astra | Broad computer-use surface, async tools, steering, and strong reported OSWorld results. | Confirm permissions, confirmation policy, latency, and side-effect recovery. |
| Repository-scale coding and migrations | GPT-6 Astra, with Fable 5.1 as a serious alternative | Astra leads the selected coding scores; Fable remains strong for long unattended sessions and root-cause work. | Replay the same repository, tests, tool loop, and rollback process. |
| Research memo, spreadsheet, or slide deck | Fable 5.1 for artifact-first work; Astra for browser-to-artifact workflows | Fable is explicitly optimized for long knowledge work; Astra is strong at end-to-end computer use and document creation. | Compare citation recall, layout quality, tool turns, and human edits. |
| Cache-heavy repeated context | Fable 5.1 | $0.25/MTok cache reads and standard pricing across its 1M window. | Check cache hit rate, write lifetime, tokenizer usage, and gateway billing. |
| Safety-sensitive defensive security work | GPT-6 Astra with approved safeguards | OpenAI documents stronger cyber capability and additional monitoring. | Use authorized targets, least privilege, audit logs, and human approval. |
| Multilingual writing or localization | No automatic winner | Neither provider’s release establishes a decisive new language advantage. | Run the same terminology-controlled corpus in every target language. |
| High-throughput or strict low-latency service | A smaller model may be better | Both are frontier-priced and effort-intensive; Fable is documented as slower, while Astra latency depends on mode and harness. | Measure p50/p95, first output, cost per successful task, and fallback rate. |
Model position on September 4, 2026
The current model landscape is better described as a two-axis map than a single leaderboard.
- GPT-6 Astra is the broad flagship. Its public API, computer-use workflow, tool orchestration, science and coding results, long context, and safety controls place it at the front of general end-to-end agent work. OpenAI’s current model guidance tells developers to start with Astra for the hardest reasoning and coding tasks.
- Claude Fable 5.1 is a co-leading specialist and a practical alternative. It remains near the frontier on coding and knowledge work, wins some published evaluations, and has unusually favorable cache-read economics. It is not merely a legacy model waiting for replacement.
- “Best” depends on the unit of work. If the unit is a browser task completed under authorization, Astra’s tool and safety stack may dominate. If the unit is ten long research turns that reread a stable 1M-token prefix, Fable’s cache economics may dominate. If the unit is a finished artifact, human correction time can be more important than either raw score.
- Access is part of model status. A model can be a global frontier release while still being unavailable through a particular gateway, account, region, or protocol. Availability must be checked separately from capability.
Modelflare availability and integration boundary
The live Modelflare catalog was checked on September 4, 2026 through https://modelflare.dev/api/pricing. At that time, claude-fable-5-1 was listed with Anthropic- and OpenAI-compatible surfaces and three public routing groups. The same response did not list gpt-6-astra. This article therefore makes no Modelflare GPT-6 access or price promise; the live pricing page and model list remain the product source of truth.
| Modelflare catalog item | Current observation | User-facing input / output | Position |
|---|---|---|---|
| claude-award + claude-fable-5-1 | Listed, ratio 0.25× | $2.50 / $12.50 per 1M tokens | Lowest current Fable route; use when retryable work can tolerate variability. |
| claude-stable + claude-fable-5-1 | Listed, ratio 0.315× | $3.15 / $15.75 per 1M tokens | Balanced daily and production route. |
| claude-premium + claude-fable-5-1 | Listed, ratio 0.5× | $5.00 / $25.00 per 1M tokens | Stability-first Fable route. |
| gpt-6-astra | Not listed in the checked response | No public Modelflare price at check time | Do not hard-code access before the live catalog and a real endpoint probe agree. |
For Fable 5.1, the existing Modelflare Fable 5.1 deep dive covers route selection, Messages requests, and Fable-specific migration detail. For protocol choices, see Responses API migration, Responses API vs Chat Completions, and reliable AI API routing.
If and when GPT-6 Astra appears in the Modelflare catalog, use the exact provider ID gpt-6-astra, verify the selected group, and test the endpoint your client actually needs. OpenAI’s guidance recommends the Responses API for tool calling and says to remove unsupported parameters such as temperature and top_p when migrating. A successful model-list response alone does not prove streaming, tools, structured outputs, or computer use.
Limits, caveats, and responsible use
- Parameter uncertainty: total and active parameter counts are undisclosed. Context length, output length, and benchmark scores are not parameter counts.
- Benchmark uncertainty: the scores above are provider-reported or reproduced in a provider launch table. Harnesses, prompts, effort, tools, safety filters, and task versions can change the result.
- Protocol uncertainty: a model ID does not guarantee identical fields or event streams across Responses, Chat Completions, Anthropic Messages, Bedrock, Google Cloud, or a gateway compatibility layer.
- Freshness: both models have dated knowledge cutoffs. Use retrieval for current facts and record the source date.
- Safety: stronger models can make higher-impact mistakes. Restrict tools, separate planning from execution, require confirmation for irreversible actions, and keep an external audit trail.
- Pricing and access: provider prices, cache rules, routing groups, and availability can change. Recheck the live provider and Modelflare pages immediately before a production decision.
FAQ
Is GPT-6 Astra better than Claude Fable 5.1? GPT-6 Astra is the stronger broad flagship for end-to-end computer use, tool orchestration, coding, and science in the published launch evidence. Fable 5.1 remains competitive and leads some evaluations; it can be the better choice for cache-heavy, long-horizon knowledge work.
How many parameters does GPT-6 Astra have? OpenAI has not published a verified total or active parameter count on the official model and launch pages reviewed for this article. Do not treat an online estimate as an official specification.
Which model is cheaper? Their base input/output rates are both $10/$50 per million tokens. Fable 5.1 is cheaper for cache reads, while Astra has its own Batch, Flex, and Fast modifiers. The cheaper completed task depends on token volume, cache hits, tool turns, latency, and retries.
Which model is better for coding agents? Start with GPT-6 Astra when the agent must browse, edit, test, and coordinate tools. Consider Fable 5.1 when the agent runs for a long time over a stable context or already uses the Claude Messages contract. Replay the same repository and tool loop before switching.
Is GPT-6 Astra available on Modelflare? It was not listed in the live Modelflare pricing response checked on September 4, 2026. Official OpenAI release status and gateway availability are separate facts; check /pricing and the model list for a later update.
Do both models have a 1M-token context window? Yes, both provider pages document a 1M-class window: 1.05M for GPT-6 Astra and 1M for Fable 5.1. Their billing, tokenization, retrieval behavior, and protocol limits are not identical.
Sources and update record
- OpenAI: GPT-6 Astra — A new generation of intelligence
- OpenAI API: GPT-6 Astra model
- OpenAI API: GPT-6 Astra model guidance
- OpenAI: Safety overview for GPT-6 Astra
- Anthropic: Introducing Claude Fable 5.1 and Claude Mythos 5.1
- Claude Platform: Fable 5.1 overview
- Claude Platform: What’s new in Fable 5.1
- Claude Platform: Fable pricing
- Modelflare live models and pricing
Update record: September 4, 2026 — initial publication package. Official GPT-6 Astra and Fable 5.1 specifications, launch comparisons, pricing rules, and availability statements were rechecked. The Modelflare catalog was checked at the same date; Fable 5.1 was listed and GPT-6 Astra was not. No independent benchmark reproduction or undisclosed parameter claim is made.