Claude Opus 5.5 vs GPT-6 Astra: Which to Build With

Claude Opus 5.5 vs GPT-6 Astra pits Anthropic's newest Opus against OpenAI's flagship. Astra launched on September 3, 2026, and Opus 5.5 followed on September 22, at well under half the price.

On paper, this looks like a rout. Opus 5.5 scores higher on Artificial Analysis's independent index, a composite of 10 evaluations that ran both models on one harness. It also costs less per token in every billing category.

The real picture is more interesting. Astra finishes tasks with a fraction of the tokens, it wins a few specific kinds of work, and its pricing has a second tier most comparisons skip. This guide covers all three so you can pick the right model for what you are building.

Opus 5.5 scores higher for less, and Astra's case is narrower

The capability lead belongs to Opus 5.5. On the Opus 5.5 benchmarks, it holds the top spot on the Artificial Analysis Intelligence Index at 58, while Astra lands at 53, both at maximum effort.

The price gap favors Opus too. Astra charges 2.5 times more for fresh input and output, and five times more for cached input.

Where it gets closer is cost per finished task. Astra reaches its answers with far fewer output tokens, so a single Astra task can cost less than a single Opus task at the same effort label. The fair test is to compare the two at the same quality level instead, and from a mid-range score upward Opus 5.5 still comes out ahead.

What each model is, and what comes with it

Both models are flagships, but they arrive with different release dates, strengths, and safeguards.

1. Opus 5.5 is Anthropic's current Opus

Claude Opus 5.5 launched on September 22, 2026, replacing Opus 5. Anthropic built it for long-running agentic coding and knowledge work, and the Opus 5.5 launch came with a 20% price cut over Opus 5. It carries a 1 million-token context window.

2. GPT-6 Astra is OpenAI's flagship

GPT-6 Astra launched on September 3, 2026, and sits above GPT-6 Sol and GPT-6 Luna in OpenAI's lineup. OpenAI positions it as its strongest model for computer use, software engineering, and science. It also has a 1 million-token context window. The GPT-6 Astra benchmarks breakdown covers its launch scores in full.

3. Both ship with safeguards that change what you get

Astra meets the Critical cybersecurity threshold under OpenAI's Preparedness Framework. Its most sensitive cyber features sit behind a trusted-access program, and enterprise admins must switch the model on before their teams see it.

Opus 5.5 handles the same risk differently. The Opus 5.5 announcement says most cybersecurity tasks are re-routed to Claude Opus 4.8, while flagged biology and frontier AI development work falls back to Opus 5. Routine bug fixing in your own code stays on Opus 5.5. For builders in those sensitive domains, the model you request is not always the model that answers.

Opus leads from medium effort up, and Astra leads at low

Both models let you set an effort level that controls how long they reason. Artificial Analysis ran both at every level on the same harness, which makes it the cleanest like-for-like comparison available. Its Intelligence Index combines 10 evaluations, so treat it as a composite signal rather than a single test.

Table 1 - Artificial Analysis Intelligence Index scores and cost per benchmark task at each effort level. Opus 5.5 results use Anthropic's default fallback setting. Cost per task is the weighted cost of one index task, not a general API cost. As of September 2026. Source: Artificial Analysis.

Two patterns stand out. Astra is the stronger model at low effort, 46 to 42, which matters for fast, cheap calls. From medium upward, Opus 5.5 scores higher at every level, and its lead widens to five points at max.

The lead holds across all six of Artificial Analysis's industry indexes, by four to nine points.

Table 2 - Artificial Analysis capability indexes at maximum effort. Opus 5.5 results use Anthropic's default fallback setting. As of September 2026.

Speed also favors Opus 5.5. It generates 79 to 92 tokens per second across the four effort levels Artificial Analysis measured, against 47 to 55 for Astra. In Artificial Analysis's max-effort run, Astra took about six minutes before its first token appeared, so at that setting it suits background work more than live chat.

What each vendor's own table shows

Each company published benchmarks on its own setup, and the two tables barely overlap. Anthropic's launch table lists Astra figures as reported by OpenAI, not re-run by Anthropic.

Table 3 - Vendor-reported scores from Anthropic's launch table. Opus 5.5 ran at max effort, except Terminal-Bench 4.0 at xhigh. Astra figures are as reported by OpenAI, with its Terminal-Bench 4.0 score at high effort. Harnesses and settings may differ, so treat gaps as directional. As of September 2026.

The pattern matches the independent read. In these vendor-reported figures, Opus 5.5 leads on knowledge work and terminal coding, while FrontierCode is close enough to call even. Astra leads on agentic science and narrowly on business automation. AutomationBench was run by Zapier, which counted Opus safeguard interventions as failures rather than letting a fallback model finish.

OpenAI's own GPT-6 Astra announcement focuses on computer use, where Astra scores 72.6% on OSWorld 2.0 in about 40 minutes per task. Anthropic reports a partial-credit OSWorld 2.0 result for Opus 5.5 that uses different scoring, so the two figures cannot be compared directly.

Pricing: 2.5x per token, but token use changes the bill

The rate card is lopsided, but the bill depends on how many tokens each model spends.

1. Astra costs 2.5x more on the rate card, and 5x more on cache reads

Table 4 - Standard API pricing for requests under 272,000 input tokens. Sources: Anthropic pricing and OpenAI pricing, as of September 2026.

The cache-read row is the one to watch. Agents resend the same instructions and history every turn, so much of their input often comes from cache, and there Astra costs five times as much. Measure your own cache-hit rate before relying on this.

Take one agent request of 250,000 input tokens, 90% of them from cache, with 10,000 output tokens. On Opus 5.5 it costs about $0.35. On Astra it costs about $0.98. In this example, caching widens the gap to roughly 2.8x instead of narrowing it.

Want the full breakdown? Read our Claude Opus 5.5 pricing guide before you commit.

2. Astra's second price starts above 272,000 tokens

Astra has a long-context tier. Once a single request passes 272,000 input tokens, OpenAI pricing bills the whole call at $20 input, $2 cached input, $25 cache write, and $75 output per 1 million tokens. Opus 5.5 charges the same standard rates across its full 1 million-token window.

The step matters for anything that reads a large codebase or document set. A 250,000-token request with 10,000 output tokens stays under the line, so Astra bills it at standard rates: about $3.00, against $1.20 on Opus 5.5, a 2.5x gap. Grow that request to 300,000 tokens and Astra jumps to about $6.75, while Opus 5.5 rises to $1.40. The gap nearly doubles to 4.8x.

3. Astra spends fewer tokens, but Opus still wins at matched quality

Here is where Astra claws back ground. At max effort, Artificial Analysis measures Opus 5.5 generating about 119,000 output tokens per task, against about 27,000 for Astra. That is why Astra at max costs $3.26 per task while Opus 5.5 at max costs $5.98, despite the rate card.

Comparing both models at the same effort label hides the real trade-off, though. The better test is what each model costs to hit the same score.

Table 5 - The cheapest setting on each model that reaches a given Artificial Analysis index score. As of September 2026.

The result is clear. Astra is the cheaper route only to a mid-40s score, through its low setting. From 51 up, Opus 5.5 gets there for less, and above 53 it is the only one of the two that gets there at all. For Astra's full rate details, see the GPT-6 Astra pricing guide.

Where Astra still earns its premium

Astra's advantages are real, just narrow. It is worth the higher price in four situations.

Scientific reasoning is the clearest one. Astra leads Terminal-Bench-Science by about six points in Anthropic's own table.

Business automation across many tools is the second, with Astra narrowly ahead on AutomationBench. Computer use is the third, where OpenAI reports its strongest results, though no shared benchmark settles it against Opus 5.5.

The fourth is fast, low-effort work, where Astra scores higher than Opus 5.5 at its cheapest setting. If your team already runs on OpenAI's Codex and tooling, staying in that ecosystem is a fair reason as well. The Astra vs Fable 5.1 comparison shows how Astra fares against Anthropic's higher tier on these same strengths.

Which model to pick for which job

Match the model to the work, then tune the effort setting within it.

Table 6 - Which model fits which job.

Before you commit, run a few of your real tasks through both at the effort settings you would actually ship. If Astra's price is the sticking point but you want to stay with OpenAI, the GPT-6 Sol benchmarks show what the tier below delivers.

Build with either model on Emergent

If you are describing an app rather than coding it, the model choice becomes a setting you can change per project. Emergent builds full-stack apps from a plain-language description, and both Claude Opus 5.5 and GPT-6 Astra are available to build with. Opus 5.5 went live on Emergent on launch day.

Access runs through one Universal LLM Key, which covers Claude, GPT, and Gemini models with unified billing from your Emergent credits.

Start Building on Emergent and put the right model behind each part of your app.