For Claude Opus 5.5 vs Grok 4.7 vs GPT-6 Astra, my short answer is this: choose Opus 5.5 for long agentic coding work if you can pay for it. Choose Grok 4.7 when cost per token matters most. Choose GPT-6 Astra when you need computer use, the largest context window, and cost matters less. Astra 6.1 never shipped.
Is Astra 6.1 out?
No. GPT-6.1 Astra, also called Astra 6.1, was planned for October but cancelled after internal safety testing. The Wall Street Journal reported the cancellation on 28 September 2026. OpenAI also confirmed it to Reuters, according to The Next Web.
Saachi Jain, OpenAI's head of safety systems, said the model "didn't quite meet the bar in terms of staying within scope and authorization, and how it communicates back to the user about the type of work it's done". Reported problems included deceptive behaviour, inaccurate accounts of completed work, and taking action without asking permission.
The shipping model is GPT-6 Astra, released on 3 September 2026. OpenAI's current model list names it as the flagship, alongside GPT-6.1 Sol and GPT-6 Luna. This article therefore compares GPT-6 Astra, not an unreleased successor.
Claude Opus 5.5 vs Grok 4.7 vs GPT-6 Astra at a glance
- claude-opus-5-5: released 22 September 2026. It has a 1M token context window and 128K maximum output. Base prices are $4 input and $20 output per million tokens.
- grok-4.7: released 21 September 2026. It has a 500,000 token context window. Base prices are $2 input and $6 output per million tokens.
- gpt-6-astra: released 3 September 2026. It has a 1,050,000 token context window, 922,000 maximum input, and 128,000 maximum output. Base prices are $10 input and $50 output per million tokens.
Which model scores best on coding benchmarks?
On Terminal-Bench 4.0, Anthropic reports Opus 5.5 at 66.4% with xhigh effort and a standard error of plus or minus 2.6 points. xAI reports Grok 4.7 at 37.6% with xHigh effort. Anthropic's table lists GPT-6 Astra at 57.9%, using OpenAI's reported result.
The neutral public Terminal-Bench 4.0 leaderboard gives more detail for Astra in Codex: 58.2% at max effort, with a run cost of $3,267, and 57.9% at high effort, with a run cost of $2,269. Its early September snapshot did not yet contain Opus 5.5 or Grok 4.7. It listed Claude Code with Fable 5.1 at 57.9% at max effort, which is not an Opus 5.5 result.
On CursorBench 4.0, Anthropic reports Opus 5.5 at 57.8% with max effort. xAI reports Grok 4.7 at 46.3% with xHigh effort. OpenAI has not published a CursorBench 4.0 figure for Astra, and Anthropic's table leaves that cell empty.
These are not clean head-to-head tests. They come from different harnesses and effort levels, and most are vendor-reported. The only neutral source here is the public Terminal-Bench leaderboard, which includes Astra but not the other two. Treat gaps of a few points as noise. Our earlier article explains why the coding agent harness can change the result.
How much does each model cost per million tokens?
Opus 5.5 costs $4 for input and $20 for output. Cache reads cost $0.20, while cache writes cost $5 for five minutes or $8 for one hour. Its Batch API gives a 50% discount.
Grok 4.7 costs $2 for input, $0.50 for cached input, and $6 for output. For a request above 200K context, xAI's release notes give higher rates of $4, $1, and $12 respectively. Batch processing is not supported.
GPT-6 Astra costs $10 for input, $1 for cached input, and $50 for output. A prompt above 272K input tokens prices the full request at twice the input rate and one and a half times the output rate. Batch and Flex give a 50% discount.
Consider an uncached task that uses one million input tokens and 100K output tokens. Assume the work is split into requests below each long-context threshold, so base rates apply. Opus costs $6: $4 for input and $2 for output. Grok costs $2.60: $2 for input and $0.60 for output. Astra costs $15: $10 for input and $5 for output. This is token arithmetic, not a measure of how many attempts each model needs.
Which is best for tool use and agentic coding?
Claude Opus 5.5
Opus 5.5 is built for long-running agentic coding, according to Anthropic's model page, and its 1M token context leaves room for large repositories. Its thinking mode cannot be disabled. Forced tool use now returns an error, which is a breaking change from Opus 5. Agent code that assumes a forced tool choice will need adjustment.
Anthropic says every action is screened by a classifier before it runs in its coding agent. It also says most cybersecurity tasks are rerouted to Opus 4.8. That policy matters if your evaluation includes security work.
Grok 4.7
Grok accepts text and image input, returns text, and supports function calling, structured output, and low, medium, high, or xhigh reasoning. It was trained to understand the Grok Bot harness. It is available through Cursor, Grok Build, the API, and Amazon Bedrock. The missing Batch API is a practical limit for offline, high-volume work.
GPT-6 Astra
Astra has the broadest listed hosted tool set. Its Responses API tools include hosted shell, apply_patch, computer use, MCP, and tool search. Tool calling requires the Responses API. If an agent must operate a graphical interface as well as a repository, Astra has the clearest fit from these published features.
How fast are they?
There is no comparable vendor set of tokens-per-second figures. Anthropic says Opus 5.5 fast mode runs at up to 2.5 times the speed, costs $8 input and $40 output per million tokens, and produces output more than 30% faster than Opus 5.
Grok's fast variant offers twice the output speed at twice the price. According to xAI's release notes, it is limited to Cursor and Grok Build, not the public API. Astra fast mode costs twice the base rates. OpenAI also reports an OSWorld 2.0 score of 72.6% at roughly 40 minutes per task, but that is task time, not a general generation speed.
Which model should you pick?
- Long unattended refactors: start with Opus 5.5. Its strong agent benchmark results, large context, and lower base price than Astra make it my first test. Anthropic also claims that it matches Astra on Terminal-Bench at about 40% of the cost.
- High-volume agents on a budget: start with Grok 4.7. Its base token price is clearly lower. Check task completion rates before assuming the cheapest token gives the cheapest completed task.
- Computer use and huge context: choose Astra. It has the largest listed context and the clearest hosted computer-use support. Accept the higher token bill.
- Regulated or security-heavy work: test policy behaviour, audit logs, permission handling, and refusal paths. Opus reroutes most cybersecurity tasks to an older model, so confirm that this is acceptable before selection.
My view is simple. No single model wins every row here, and the published numbers are too uneven to crown one. Run your own evaluation on 20 to 30 real tasks. Include failed tool calls, permission requests, incomplete work, retries, latency, and total cost. That evidence matters more than a small vendor benchmark lead.
Where Alongside fits
Picking a model, running the evaluation on your own code, and setting cost controls is the kind of work covered by Alongside's technical advisory.