GPT-6 Astra vs Claude Fable 5.1 vs Opus 5.5 for Coding: Benchmarks and Real Cost

The short answer: for most coding work, Claude Opus 5.5 is now the best value of the three. It lists at $4 / $20 per million tokens against $10 / $50 for both GPT-6 Astra and Claude Fable 5.1, and it posts the highest independent Intelligence Index score so far. GPT-6 Astra is the cheapest per finished task when you run it at max effort, because it writes far fewer tokens. Fable 5.1 ties Astra on coding quality but costs the most per task, so it only makes sense if you already have it bundled in a Claude Max or premium seat.

Astra and Fable 5.1 cost the same per token, yet Astra finished Artificial Analysis's test tasks with about a third of the output tokens. Opus 5.5 is 60% cheaper per token than Fable 5.1 but is the most verbose of the three at max effort. What you actually pay depends on tokens used per task and on how much of your prompt you can cache.

This guide was last checked against OpenAI's API model docs, Anthropic's pricing and model docs, Anthropic's Opus 5.5 announcement, the Claude and OpenAI help centers, and Artificial Analysis benchmark reports on September 24, 2026.

Quick summary

• API list price: Astra and Fable 5.1 are both $10 input / $50 output per million tokens. Opus 5.5 is $4 / $20 (Anthropic pricing, OpenAI model docs).
• Cost per task (independent): On the Artificial Analysis Intelligence Index at max effort, Astra averaged $3.26 per task, Opus 5.5 $5.98 and Fable 5.1 $7.63.
• Coding quality (independent): Astra and Fable 5.1 are tied at 62 on the Artificial Analysis Coding Agent Index. Opus 5.5 reportedly scores 66, but at a higher cost per task (details and caveats below).
• Vendor headline numbers: Anthropic reports Opus 5.5 at 66.4% on Terminal-Bench 4.0 against 57.9% for Astra and 55.8% for Fable 5.1. Artificial Analysis's own run has Opus 5.5 and Astra level at 59.6%.
• Caching: Cache reads cost $1.00 per million on Astra, $0.25 on Fable 5.1 and $0.20 on Opus 5.5. For agent loops that resend a large repo context, this gap matters more than the list price.
• Context: All three have roughly 1M-token windows and 128K max output. Astra charges 2x input and 1.5x output on prompts over 272K tokens. Claude models do not add a long-context surcharge.

The real cost: price per completed coding task

Per-token prices tell you what a token costs. They don't tell you how many tokens a model burns to close a ticket. Artificial Analysis counts both, and the gap between the three models is large.

The token counts come from Artificial Analysis's Opus 5.5 write-up: about 119k output tokens per task for Opus 5.5 (max), about 78k for Fable 5.1 (max) and about 27k for Astra (max). The "output-token cost" row is simply those counts multiplied by each model's list output price, ignoring input and cache costs, so it is a floor.

The cost-per-task and score rows come from Artificial Analysis's Astra benchmark report ($3.26 for Astra against $7.63 for Fable 5.1, both scoring 53) and its Opus 5.5 vs Astra comparison ($5.98 for Opus 5.5 at max, scoring 58).

On the coding-specific index, Artificial Analysis measured Astra in Codex at $7.09 per task and said it was about 40% cheaper than Fable 5.1 in Claude Code for the same score of 62. It did not print Fable 5.1's dollar figure in that report, so we don't either.

Artificial Analysis Intelligence Index, max effort

Two things stand out.

Astra is the frugal one. It writes about a third of the output tokens Fable 5.1 does. Artificial Analysis also noted Astra needed far fewer agent turns on some tasks (24 at max effort against 45 to 60 for the others). At the same $50 output price, that alone makes it cheaper per task than Fable 5.1.

Opus 5.5's low price is partly spent on verbosity. At max effort it uses more output tokens than Opus 5 (about 119k against 73k), and it lands at a similar cost per task to Opus 5, not a lower one. Anthropic's "about 40% cheaper than Opus 5 on typical workloads" claim assumes you are not running max effort on everything. Opus 5.5's API default is medium effort, and at low effort Artificial Analysis measured it at $0.55 per task against $0.82 for Astra at low.

Benchmark table: what each number actually is

Coding benchmarks for these three models come from three different places, and they disagree. OpenAI and Anthropic each publish their own tables, run on their own harnesses and effort settings. Artificial Analysis runs everything itself. Every cell below names its source.

Notes on the sources, row by row:

• Terminal-Bench 4.0, vendor. OpenAI's own figure for Astra is 57.7%, as reported by LLM Stats. Anthropic's Opus 5.5 announcement lists Astra at 57.9% (high effort), Fable 5.1 at 55.8%, Opus 5 at 52.3% and Opus 5.5 at 66.4% (extra-high effort). Anthropic itself cautions that a few points at this level don't translate into a noticeable difference in real use.
• Terminal-Bench 4.0, independent. Artificial Analysis measured Astra at 59% at max effort (vs 52% for Fable 5.1) in its Astra report. In its Opus 5.5 report it has Opus 5.5 at 59.6%, "level with the leader GPT-6 Astra (xhigh)", and about 11 points ahead of Opus 5. Anthropic's 7-point lead over Astra largely disappears on a neutral harness.
• Coding Agent Index. The 62/62 tie between Astra and Fable 5.1 is from Artificial Analysis's Astra report. The Opus 5.5 (66) and Opus 5 (60) scores, and the $13.04 cost per task, come from an OrcaRouter summary citing Artificial Analysis. We could not see those figures on Artificial Analysis's own Opus 5.5 page at the time of checking, so treat them as reported, not confirmed. The same summary lists Astra at 53, which conflicts with Artificial Analysis's published 62, and we have left that figure out.
• FrontierCode and CursorBench. These are Anthropic's numbers. OpenAI reports a different variant, FrontierCode 1.1 Extended, where Astra scores 64.5. The two are not comparable.
• DeepSWE. Astra's 74.1% is OpenAI's figure. Fable 5.1's 67.4% comes from a DataCamp comparison and we could not trace it to an Anthropic table.
• SWE-bench. OpenAI did not publish SWE-bench Pro for Astra, and Anthropic's Opus 5.5 table does not include SWE-bench Verified or Pro. A SWE-bench Pro figure for Opus 5.5 is circulating in secondary coverage, but it is not in Anthropic's published table, so we have left it out.

One caveat specific to Opus 5.5: Anthropic says it was evaluated with production safeguards on, and when those intervened, cybersecurity tasks were handed to Claude Opus 4.8. That matters if your "coding" includes exploit or security tooling work. We covered it in Claude Opus 5.5 is Anthropic's strongest cyber model, so most security work goes to Opus 4.8.

Speed and latency

All three are reasoning models, so "speed" has two parts: how long before the first visible token, and how fast tokens stream after that. Figures below are Artificial Analysis measurements at max effort.

Opus 5.5 streams fastest, and Anthropic says it generates output more than 30% faster than Opus 5. Astra streams slowest but writes much less, so wall-clock time per task is closer than the tokens-per-second figure suggests. The very long time-to-first-token numbers reflect max-effort thinking time. At lower effort they drop sharply; Artificial Analysis measured Opus 5.5 at low effort at 5.18 s.

Both vendors sell a faster tier. OpenAI's docs say fast mode costs 2x the applicable rates. Anthropic's fast mode for Opus 5.5 is $8 input / $40 output on the Claude API only, and is not offered for Fable 5.1.

Context windows and long prompts

Sources: OpenAI's Astra model page and Anthropic's Opus 5.5 overview, which also lists Fable 5.1's specs.

The 272K threshold is the one to watch. If you feed Astra a large monorepo slice, the whole request is billed at $20 input and $75 output per million. Anthropic's docs say a 900K-token request on current Claude models bills at the same per-token rate as a 9K one.

Caching economics for agent loops

Coding agents resend the same system prompt, tool definitions and file context on every turn. That makes cache-read price one of the biggest levers on your bill. We explain the mechanics in Prompt caching explained.

Here is a worked example using those list prices. An agent loads a 150K-token repo context, writes it to cache once, then reads it back 40 times during a session. That is 6M cached input tokens.

Fable 5.1's $0.25 cache read claws back a lot of what it loses on output verbosity, and it is why Fable can look reasonable for long, context-heavy sessions despite its high sticker price. Astra's cache reads cost four to five times more than either Claude model. On context-heavy agent work, that erodes some of the advantage it wins by writing fewer tokens.

If caching is new to your stack, the migration math in Enterprise LLM pricing and the economics of model migration is worth running before you switch vendors.

Which subscription plans include each model

Sources: OpenAI's help center on Astra usage in Work and Codex and Anthropic's help center on Fable models by plan. Astra in Codex needs Codex CLI 0.153.0 or newer; Fable 5.1 in Claude Code needs version 2.1.255 or later. For the CLI landscape itself, see how Claude Code, Codex CLI, Antigravity CLI and OpenCode compare on price, models and sandboxing. Anthropic also raised five-hour usage limits on Pro, Max, Team and seat-based Enterprise plans at the Opus 5.5 launch, without publishing the new numbers.

For the plan-level detail, see our companion guides on Codex and GPT-6 usage limits by ChatGPT plan and Claude Pro vs Max for Claude Code after Opus 5.5.

Which should you pick?

Pick Claude Opus 5.5 if you want the best price-to-quality ratio for everyday agentic coding. It has the lowest list price, the cheapest cache reads, no long-context surcharge and the fastest output. Keep effort at medium or high for routine work. Save max effort for hard tasks, because that is where its token use balloons.

Pick GPT-6 Astra if you run max-effort, output-heavy jobs where cost per completed task matters most, or you already live in Codex. It writes the fewest tokens of the three and had the lowest Intelligence Index cost per task at max effort. Keep prompts under 272K tokens where you can, and don't expect caching to save as much as it does on Claude.

Pick Claude Fable 5.1 if it is already included in your Claude Max plan or a premium Team seat, or you have a workflow tuned to it. It ties Astra on the Coding Agent Index and its cache reads are cheap. On the raw API, though, Anthropic's own table has Opus 5.5 ahead of it on every coding benchmark at 40% of the per-token price. Few new API projects should start on Fable 5.1 today.

Stay on a smaller model if most of your traffic is simple edits and test fixes. For a specific open-weight contender, see whether Kimi K3 can really replace Claude and Codex as your coding assistant. Open-weight models have closed much of the gap on routine coding, as we found in our Laguna S 2.1 coding benchmark analysis.

Caveats before you commit

• Vendor benchmarks are marketing. Anthropic's 66.4% Terminal-Bench figure for Opus 5.5 came out as 59.6% on Artificial Analysis's harness. We saw the same pattern when Grok 4.7 matched Fable 5.1 Max on SpaceXAI's own scorecard.
• Opus 5.5 is two days old. Independent coverage is thin. Artificial Analysis has published an Intelligence Index score and Terminal-Bench result; the Coding Agent Index numbers are so far only visible second-hand.
• Harness matters. Artificial Analysis measured Astra inside Codex and the Claude models inside Claude Code. Your own agent framework may favor a different model.
• Effort settings change everything. A cost-per-task figure only holds at the effort level it was measured at.
• The GPT-6 family is wider than Astra. OpenAI's cheaper GPT-6 Sol and Luna launched on September 22; see GPT-6 launches with near-frontier intelligence at half the price.

FAQ

Is GPT-6 Astra better than Claude Fable 5.1 for coding?

They are effectively tied. Artificial Analysis scores both at 62 on its Coding Agent Index. Astra scored higher on its Terminal-Bench 4.0 run (59% against 52%) and costs about 40% less per coding task because it writes far fewer tokens. Fable 5.1 has much cheaper cache reads ($0.25 against $1.00 per million).

What is GPT-6 Astra's API pricing?

$10 per million input tokens, $50 per million output tokens, $1 per million cached input tokens and $12.50 per million for cache writes, per OpenAI's model docs. Prompts over 272K input tokens are billed at 2x input and cache rates and 1.5x output for the whole request. Batch and Flex are 50% off.

How much does Claude Fable 5.1 cost?

On the API, $10 input and $50 output per million tokens, with cache writes at $12.50 (5-minute) or $20 (1-hour) and cache reads at $0.25. In the Claude apps it is included on Max plans for up to 50% of the weekly limit, and billed as usage credits on Pro.

Is Opus 5.5 better than Fable 5.1?

On Anthropic's own benchmarks, yes: it leads Fable 5.1 on Terminal-Bench 4.0, FrontierCode and CursorBench, at 40% of the per-token price. Independently, Artificial Analysis gives Opus 5.5 an Intelligence Index of 58 against 53 for Fable 5.1. The trade-off is that Opus 5.5 at max effort uses more output tokens.

GPT-6 Astra vs Opus 5.5: which is cheaper?

Per token, Opus 5.5 by a wide margin ($4 / $20 against $10 / $50). Per task at max effort, Astra was cheaper on the Artificial Analysis Intelligence Index ($3.26 against $5.98) because it writes about a quarter of the output tokens. At low effort, Opus 5.5 was cheaper ($0.55 against $0.82).

Are there independent Opus 5.5 benchmarks yet?

A few. Artificial Analysis has published an Intelligence Index score (58 at max effort), a Terminal-Bench 4.0 result (59.6%) and speed figures. Its Coding Agent Index score for Opus 5.5 (66) has so far only appeared in third-party summaries. Everything else, including the 66.4% Terminal-Bench figure, is Anthropic-reported.

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