GPT-6 Astra vs Claude Fable 5.1: Agentic Coding Benchmarks Tested (September 2026)
Two frontier flagships shipped inside 72 hours. OpenAI released GPT-6 Astra on September 3, 2026, calling it the most intelligent and aligned model it has published, and Anthropic released Claude Fable 5.1 on September 1 with a 75 percent cut to cache read pricing. Both carry a 1 million token context window and both list at 10 dollars per million input tokens and 50 dollars per million output tokens, so the sticker price will not decide this for you. What decides it is the agentic coding numbers and the cost you actually pay once caching enters the picture. On Terminal-Bench 4.0, Astra reads 57.7 percent against Fable 5.1 at 55.8 percent; on DeepSWE v1.1, Astra is 74.1 percent. Those gaps are real but narrow, and narrow gaps get erased by the parts of the bill that the leaderboard never shows. This post puts the two side by side on the benchmarks that matter for coding agents, then argues for a default based on total cost per solved task rather than a headline percentage.
GPT-6 Astra vs Claude Fable 5.1: Agentic Coding Benchmarks Tested (September 2026)
Two frontier flagships shipped inside a single 72 hour window. Anthropic released Claude Fable 5.1 on September 1, 2026, and OpenAI released GPT-6 Astra on September 3, calling it the most intelligent and aligned model it has ever published. Both run a 1 million token context, both are priced identically at the sticker level, and both are aimed squarely at agentic coding. When two models arrive this close together at the same price, the interesting question is not which one wins a benchmark by two points; it is which one you should set as the default engine for an agent that runs thousands of times a day.
| Attribute | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Released | September 3, 2026 | September 1, 2026 |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Input price | about 10 dollars per million tokens | about 10 dollars per million tokens |
| Output price | about 50 dollars per million tokens | about 50 dollars per million tokens |
| Cache reads | standard OpenAI cache rate | about 0.25 dollars per million (cut 75 percent from 1.00) |
| Terminal-Bench 4.0 | 57.7 percent | 55.8 percent |
| Terminal-Bench Science 0.1 | 64.6 percent | 52.6 percent |
| DeepSWE v1.1 | 74.1 percent | benchmarked separately |
| Availability | OpenAI API, Amazon Bedrock | Anthropic API, restricted Mythos 5.1 sibling |
What the benchmarks actually say
Start with Terminal-Bench 4.0, the agentic terminal execution benchmark where a model has to operate a shell, run commands, read output, and finish a real task rather than emit a plausible snippet. Astra lands at 57.7 percent and Fable 5.1 at 55.8 percent, with Claude Opus 5 trailing at 52.3 percent. A 1.9 point gap on a benchmark like this is inside the noise band you would expect from harness differences, effort settings, and the number of attempts allowed. Read it as a tie on capability, not a decisive win for Astra.
The gap that is not noise shows up on Terminal-Bench Science 0.1, where Astra reads 64.6 percent against Fable 5.1 at 52.6 percent. That is a 12 point spread, and it points at a genuine difference: Astra is stronger on the scientific and research flavored agentic tasks that benchmark stresses. If your agents spend their time in data pipelines, notebooks, and numerical work, that spread is worth attention. If they spend their time refactoring a TypeScript monorepo, it tells you almost nothing.
DeepSWE v1.1 is the third leg. It is a 113 task agentic software engineering benchmark, and OpenAI quotes Astra at 74.1 percent on it. Anthropic did not publish a like for like DeepSWE v1.1 number in the same table, which is itself a signal: vendors publish the benchmarks they win. Treat any cross vendor DeepSWE comparison assembled from two different launch posts as directional, because the two labs almost certainly ran different harnesses. We take the same cost per solved task discipline every time we line up frontier models, as in Kimi K3 versus Claude Opus 5 on cost per task, because a benchmark point that costs more to earn is not the same point.
Why the sticker price is a trap
Both models list at 10 dollars per million input tokens and 50 dollars per million output tokens. Identical sticker pricing tempts you to declare price a non factor and pick on benchmarks. That is the trap. Agent workloads do not pay the sticker rate; they pay a blended rate that depends almost entirely on caching, and this is where Fable 5.1 quietly separates from Astra.
Anthropic cut Fable cache reads by 75 percent at launch, from 1.00 dollars per million tokens to 0.25 dollars per million tokens. Anthropic frames that as roughly 25 percent lower cost on typical workloads and up to 45 percent lower on agentic ones. The reason the agentic figure is higher is structural: an agent resends a large, mostly static prefix on every step (the system prompt, the tool schemas, the repository context it already loaded), and that prefix is exactly what a cache read serves. On a loop that takes twenty steps to finish one task, the difference between paying 1.00 and 0.25 per million on that prefix compounds twenty times over.
So the real comparison is not 10 and 50 against 10 and 50. It is Astra at its standard cache economics against Fable 5.1 with cache reads a quarter of what they were a week ago. On a heavy agent loop, that can move the blended cost per solved task more than a two point benchmark gap ever will. We walk through the mechanics in prompt caching, cost, and throughput across frontier models; the short version is that your cache hit ratio is a bigger lever than your model choice.
Where each one wins
Reach for GPT-6 Astra when the work is science flavored or reasoning heavy: data engineering agents, numerical pipelines, research tooling, anything that looks like the Terminal-Bench Science task distribution. The 12 point spread there is the one benchmark gap in this comparison that is large enough to design around. Astra also earns a look on procurement grounds if you are already standardized on Amazon Bedrock, since it shipped there day one, which matters for teams with a governance requirement to keep inference inside an existing cloud contract.
Reach for Claude Fable 5.1 when the work is a high volume production agent that reuses a large context on every step. The cache read cut is not a marketing line; it changes the unit economics of exactly the workload most teams are actually running, which is a coding or support agent that reloads the same repository or knowledge base thousands of times a day. Fable 5.1 also carries the operational advantage of a restricted access sibling, Mythos 5.1, for vetted cybersecurity and life sciences organizations that need capabilities the standard safeguards constrain. That is a real deployment path, not a spec sheet footnote, if you operate in one of those regulated domains.
The honest read is that on raw agentic coding capability these two are close enough to call a tie for most codebases, and the decision should fall to cost structure and platform fit. If you cannot cache well, Astra's edge on the harder reasoning benchmarks tips it. If you can cache well, Fable 5.1's new read pricing tips it back. We benchmarked GPT-5.6 Sol the same way in where GPT-5.6 Sol actually wins: pick on cost per solved task, measured on your own repository with your own harness, because a public leaderboard scored under someone else's conditions is a hypothesis, not a decision.
FAQ
Is GPT-6 Astra better than Claude Fable 5.1 for coding? On Terminal-Bench 4.0 the two are within two points (57.7 versus 55.8 percent), which is close enough to call a tie for most codebases. Astra has a clear edge on Terminal-Bench Science 0.1 (64.6 versus 52.6 percent), so it is the better pick for science and data heavy agents. For general application code, benchmark both on your own repository.
Do they cost the same to run? The sticker prices match at 10 dollars per million input and 50 dollars per million output. The effective cost diverges because of caching. Fable 5.1 cut cache reads to 0.25 dollars per million, which lowers real cost on agentic loops by up to 45 percent, so on a cache heavy workload Fable can end up cheaper despite identical sticker rates.
What context window do they have? Both models run a 1 million token context window. That is enough to load a substantial codebase, but a large context you resend every step is a cost liability without caching, not a free capability.
Can I get GPT-6 Astra on AWS? Yes. Astra is available as gpt-6-astra in the OpenAI API and on Amazon Bedrock from launch, which matters for teams required to keep inference inside an existing cloud contract.
What is Mythos 5.1? Mythos 5.1 is the same underlying model as Fable 5.1, offered through restricted access programs for vetted cybersecurity and life sciences organizations that need capabilities the standard production safeguards otherwise constrain.
When this applies to your stack
If you are choosing a default model for a coding or research agent, the benchmark tables are the beginning of the analysis, not the end. The number that decides your bill is cost per solved task at your cache hit ratio, on your codebase, under your harness. That is exactly the kind of measurement we run when we help teams choose and wire up an agent engine. If you want a grounded read on which frontier model belongs at the center of your agent stack, and how to structure caching so the model you pick is the one you actually pay for, our AI integration and agent engineering practice does that work with real workloads rather than leaderboard screenshots.
Sources: OpenAI GPT-6 Astra analysis, Simon Willison, GPT-6 Astra benchmarks, Vellum, Claude Fable 5.1 and Mythos 5.1 cache cost cut, VentureBeat.
More from the lab.
vLLM vs SGLang: Choosing a Production LLM Inference Engine in Late 2026
vLLM and SGLang are both open source, both built to serve LLMs at production scale, and both frequently recommended for the exact same job, which is exactly why teams keep picking the wrong one. The real difference is not a benchmark number. It is which attention and caching architecture matches your actual traffic pattern, and most teams never check that before they commit infrastructure budget to one or the other.
Ollama's MLX Backend Beats Metal by 93 Percent: Apple Silicon Local Inference Tested (October 2026)
Ollama's 0.19 release notes say it plainly: on Apple Silicon, Ollama is now built on top of Apple's MLX framework to take advantage of its unified memory architecture, rather than the Metal backed llama.cpp path it shipped for years. On an M4 Max running Qwen3.5, that swap took decode speed from 57.8 tokens per second to 111.4, a 93 percent improvement, and prefill from 1,154 to 1,810 tokens per second. The catch is in the fine print: the MLX runner is in preview, it needs 32GB or more of unified memory to activate at all, and as of this release it only accelerates one model family. Here is what actually changed, what it costs to turn on, and where it still falls back silently to the slower path.
DGX Spark vs Mac Studio M5 Ultra: Local LLM Inference Benchmarked (October 2026)
The M5 Ultra Mac Studio shipping in September 2026 is a quad die chip with 1.2 terabytes per second of memory bandwidth and up to 256GB of unified memory, with a 512GB configuration arriving in late October. Against a single NVIDIA DGX Spark, it is not close: on a 27B dense model, the Mac decodes at roughly four times the speed. Cluster two DGX Spark units together over NVIDIA's GX10 networking instead, and the comparison flips for a different workload entirely, concurrent multi user serving. Neither machine is the right answer for every workload, and the decision comes down to whether you are serving one user or several at once.