Claude vs GPT for Agents: Opus 5.5, Sonnet 5.5 and GPT-6.1 Sol Compared

In October 2026, Claude vs GPT for agents mostly means Claude Opus 5.5 or Sonnet 5.5 against GPT-6.1 Sol. Sonnet 5.5 and GPT-6.1 Sol have the same list price: $2 per million input tokens and $10 per million output tokens. Opus 5.5 costs twice that. Neither vendor wins every workload, so decide on your own cases.

Below are the current lineup, what benchmarks can and cannot tell you, the enterprise data rules, a decision table by workload, and a bake-off method.

Which Claude and GPT models are current?

Both vendors replaced their lineups in the last six weeks. OpenAI shipped GPT-6 Astra on September 3, GPT-6 Sol and Luna on September 22, and GPT-6.1 Sol on September 29. Anthropic shipped Claude Opus 5.5 on September 22, then Sonnet 5.5, then Haiku 5.5 on October 7. Prices are standard API rates per million tokens, checked on October 15, 2026.

All seven support tool use, structured outputs, prompt caching, computer use, and a batch API at 50% off. Notice the price pairs: Fable 5.1 matches Astra, Sonnet 5.5 matches GPT-6.1 Sol, and Haiku 5.5 matches Luna. Opus 5.5 sits alone at $4 and $20. My read: for most agents the real contest is in the $2 and $0.10 tiers, with Opus 5.5 as the step up.

Why list prices do not predict cost per task

Matching list prices do not mean matching bills. Four mechanisms move the real cost.

Tokens per task. Steps per task can outweigh price. Anthropic says Opus 5.5 costs 40% less than Opus 5 on typical workloads, though input and output prices fell only 20%. Anthropic credits the gap to cheaper cache reads plus fewer tokens per task.

Tokenizers. Anthropic’s pricing page says its tokenizer since Claude Opus 4.7 produces about 30% more tokens for the same text than the older one. No vendor publishes a cross-vendor conversion, so count tokens from the usage fields in real responses.

Long prompts. OpenAI bills GPT-6 prompts over 272K input tokens at 2x input and 1.5x output for the whole request. Claude Opus 5.5 and Sonnet 5.5 charge the same rate up to 1M. Haiku 5.5 is the exception: prompts over 100K cost 5x more.

Caching. Anthropic says cache reads make up the majority of agentic and coding costs. GPT-6.1 Sol charges $0.10 per million cached tokens. Astra charges $1.00, so a cache-heavy agent pays 10x more for that line.

Here is a hypothetical worked example. A document agent makes 10 model calls per case. Each call carries the same 30,000-token prefix (instructions, tool definitions, policy). The first call writes it to cache and the other nine read it. Each call adds 3,000 new input tokens and returns 1,000 output tokens.

Sonnet 5.5 and GPT-6.1 Sol tie on paper. If one needs 14 calls instead of 10, its cost rises to $0.338, or 29% more. List prices do not show steps.

Now add failures. Say a person needs 8 minutes to fix a bad output, at $45 an hour loaded. That is $6.00 per failure. If Sonnet 5.5 passes 92% of cases, its cost per accepted outcome is $0.262 + (0.08 x $6) = $0.742. If Opus 5.5 passes 97%, its cost is $0.524 + (0.03 x $6) = $0.704. Opus 5.5 wins at any pass rate above about 96.4%. If a fix costs only $2, Opus 5.5 cannot win at all, because its token cost alone ($0.524) beats the cheaper model’s all-in cost ($0.422). Cutting the token side is covered in reducing agent token costs.

What do the agent benchmarks actually show?

Published agent benchmarks are good for a shortlist and poor for picking a winner. Every number below comes from a vendor launch post.

Sources: the Opus 5.5, Haiku 5.5, GPT-6 Astra, and GPT-6.1 Sol launch posts.

Three cautions. First, vendors report their own models at the best-scoring effort level. OpenAI’s tables say scores are “the maximum at any effort.” Production usually runs at default effort. Second, small gaps are noise. Anthropic measured Opus 5 at 52.3% on Terminal-Bench 4.0, OpenAI listed it at 52.6%, and the public leaderboard shows 51.8%. My rule: treat any gap under about 3 points as a tie.

Third, the famous benchmarks are fading. OpenAI no longer evaluates SWE-bench Verified, citing training contamination and an audit that found flawed tests in 59.4% of the problems it reviewed. τ-bench, a public test built for customer-service agents, exists, but these launch posts give no τ-bench results for the compared releases. For a policy-bound support agent, your own eval has to fill that gap.

Claude vs GPT for enterprise agents: platform, residency, and retention

In larger companies, the cloud contract and data terms you already have often decide before model quality does. Anthropic says Opus 5.5 and Haiku 5.5 are on AWS, Google Cloud, and Microsoft Azure. OpenAI says Astra is on Azure and Amazon Bedrock, and AWS made GPT-6.1 Sol generally available on Bedrock on September 29.

The catch: the same model can lose features when you change platforms. Claude in Amazon Bedrock does not support structured outputs, the Message Batches API, server-side tools such as code execution and web search, or the newest computer-use toolsets. On Bedrock, GPT-6.1 Sol has no server-side system tools, and Bedrock Guardrails do not work with its Responses API. Decision rule: pick the platform first, then check that model’s feature list on that platform.

Data residency. OpenAI’s API offers regional processing in the US, Europe, and the UAE. GPT-6.1 Sol supports US and EU residency only, per OpenAI’s data guide. Residency endpoints cost 10% more. Anthropic’s own API offers US-only inference at 1.1x. For EU processing of Claude, use Bedrock’s regional endpoints or EU inference profile, which carry a 10% premium over global routing.

Retention. By default, OpenAI keeps API abuse-monitoring logs for up to 30 days, and ZDR needs its prior approval. Anthropic enables ZDR through sales. Two traps apply. Batch jobs are not ZDR-eligible on either side (Anthropic keeps batch data 29 days). And Claude Fable 5.1 requires 30-day retention, so it needs Anthropic’s express authorization before a ZDR-only contract can use it. Anthropic also says flagged content may be kept up to 2 years even under ZDR. The full provider breakdown is in zero data retention for LLMs.

Safeguards. By default, Opus 5.5 sends most cybersecurity tasks to Opus 4.8, and Anthropic’s generally available models allow code review, patching, and alert triage but may block work like malware analysis. Anthropic’s Cyber Verification Program, expanded in October 2026, gives approved security teams Opus 5.5 with reduced blocking. That grant covers work on your own systems only. A client-facing product needs a separate application and approval. On the OpenAI side, the task stops when Astra’s safety checks trigger in the API. If you build security tooling, test default behavior and settle program access before you commit.

Claude vs GPT for agents by workload

This table is my judgment, built on the facts above. It tells you which pair to put in the bake-off, not who wins. Run the test before you sign anything.

Two patterns hold across rows. Bring in Fable 5.1 or Astra only for cases the cheaper model fails, and price that escalation separately. Keep business rules in your code, not in one vendor’s prompt quirks, so you can swap models on the next release. If data must never leave your own infrastructure, read open-source vs commercial LLMs first.

How do you run a model bake-off on your own cases?

A bake-off runs the same real cases through each candidate. It answers one question: which model gives the lowest cost per accepted outcome on your work? Here is the method I recommend.

1. Pull 150 to 300 real cases from the last 90 days, messy ones included in proportion. Write the expected outcome before any model runs. Acceptance tests for agents covers how to build the set.
2. Freeze the test setup. Use the same tools, the same step cap, and the same instructions, changed only for vendor syntax. Record exact model IDs, and treat any model change like a code change (change control for agents).
3. Run each model at two effort levels: the default and one step higher. Score and cost both move with effort, and Anthropic reports Opus 5.5 at default effort beating Astra’s best FrontierCode score.
4. Run every case three times. Count a case as passed only if all three runs pass. Agents are not deterministic, and customers see the bad run, not the average.
5. Grade blind. Use code checks for anything with a right answer. Use a calibrated model grader for the rest (LLM as a judge), and spot-check it by hand.
6. Log real cost and speed: input, cache-write, cache-read, and output tokens from usage, plus steps and 95th-percentile latency.
7. Decide on cost per accepted outcome and the worst failure. A cheaper model that sometimes does something unsafe loses.

Size matters. With 100 cases and a pass rate near 90%, the 95% margin of error is about ±5.9 points. At 400 cases it shrinks to about ±2.9. So a 3-point gap on 100 cases proves nothing. (That is a simple approximation. Comparing models on the same cases, pair by pair, can detect somewhat smaller gaps.) When the run is done, put the case set into evals in CI and rerun it whenever a vendor ships a new model.

Bottom Line

The Claude vs GPT for agents choice is a workload decision, not a brand decision. At the $2 tier, Sonnet 5.5 and GPT-6.1 Sol cost the same per token, so steps per task and pass rate decide. Pick your cloud first, then check which features survive on it. Run a three-pass bake-off on 150 or more real cases before you commit.

FAQ

Is GPT-6.1 Sol cheaper than Claude Sonnet 5.5?

On list price they are identical. GPT-6.1 Sol gets more expensive above 272K input tokens. On Bedrock, its US cross-Region profile costs $2.20 input and $11 output, 10% over the global price. Your real answer comes from steps and tokens per task.

Should I use Claude Fable 5.1 or GPT-6 Astra for my agent?

Both list at $10 input and $50 output, but Astra’s cached tokens cost 4x Fable 5.1’s, which adds up in long agent loops. Fable 5.1 needs Anthropic’s express authorization for ZDR. Use either as an escalation tier for hard cases, not as the default.

Can one agent use both Claude and GPT models?

Yes. You can route by step, with a cheap model for classification and a stronger one for reasoning. Anthropic pitches Haiku 5.5 as a subagent under Opus 5.5 or Sonnet 5.5. Amazon Bedrock now serves both GPT-6.1 Sol and the Claude 5.5 models, so one AWS account can cover both. Keep a shared eval set so a routing change gets tested like any other change.

Do SWE-bench Verified scores still matter?

Not much. OpenAI stopped evaluating on it, citing contamination and flawed tests, and recommends SWE-bench Pro instead. Neither the Claude 5.5 nor the GPT-6 launch posts report it. Treat Terminal-Bench, FrontierCode, and DeepSWE as directional, and confirm on your own repo.

How often should I rerun the comparison?

Rerun it before any switch and at least quarterly. Between September 3 and October 7, 2026, OpenAI shipped three GPT-6 releases and Anthropic shipped three Claude 5.5 models. You rarely need to rush: OpenAI’s lifecycle terms, as AWS summarizes them, give at least 6 months’ notice before retiring a generally available model, unless safety or compliance concerns require a faster timeline.

Three Ways to Work With TMA

Need an agent built? We deploy production AI agents in your infrastructure. Working pilot. Real data. Measurable ROI. → Schedule Demo

Want to co-build a product? We’re not a dev agency. We’re co-builders. Shared cost. Shared upside. → Partner with Us

Want to join the Guild? Ship pilots, earn bounties, share profit. Community + equity + path to exit. → Become an AI Architect