Claude Sonnet 5 vs GPT-5.5: Agentic vs Reasoning in 2026

This is a slightly lopsided matchup on paper: Claude Sonnet 5 is Anthropic's mid-tier model, while GPT-5.5 is OpenAI's flagship. Sonnet 5 pulls level with it at the very top of the effort dial while listing at well under half the per-token price, which is the headline most comparisons stop at. The measured picture is more interesting than that, and it moved in October 2026 when Artificial Analysis rebased its Intelligence Index: at every effort tier below the top, GPT-5.5 is ahead on score and cheaper per completed task. This guide works through both sides of that — benchmarks, pricing, and the kind of work each is actually built to win.

For the full feature rundown on Sonnet 5, see our Claude Sonnet 5 launch guide. And if you are choosing within Anthropic's own lineup, read Claude Sonnet 5 vs Claude Opus 4.8.

What is the difference between Claude Sonnet 5 and GPT-5.5?

They sit at different points in their vendors' lineups and are optimized for different things:

• Claude Sonnet 5 (Anthropic) is a mid-tier model built for agentic work — many tool calls, many loops, long-running automation. It is priced to run at high volume: $2/$10 per million tokens, permanently (the step-up to $3/$15 that Anthropic had scheduled for 1 September 2026 was cancelled).
• GPT-5.5 (OpenAI) is the flagship, at $5/$30 per million. On Artificial Analysis's Intelligence Index v4.3.2 it reaches the same ceiling as Sonnet 5 and gets there at lower effort — and because it is much less verbose, its measured cost per index task is lower than Sonnet 5's despite the higher rate card.

The clean mental model: agentic mid-tier vs reasoning flagship. Sonnet 5 wins on per-token price, latency and agentic tuning; GPT-5.5 wins on score at any matched effort setting and on measured cost per finished task. Which of those matters more depends on whether you are billing by the token or by the job.

How do Claude Sonnet 5 and GPT-5.5 compare on benchmarks?

Artificial Analysis publishes a separate score for each effort setting, and that detail decides this comparison. Here is the full ladder on Intelligence Index v4.3.2, captured 5 October 2026, with AA's own measured cost to complete one index task alongside each score.

Two results here are worth sitting with, because both run against the usual framing of this matchup.

The models are tied only at the ceiling. At each one's top effort tier they are separated by 0.2 points — noise, not a ranking. But GPT-5.5 reaches 36.98 at high effort, which is already above everything Sonnet 5 can produce at any setting short of max. At matched tiers the gap is about 5 points at high and about 6 at medium, consistently in GPT-5.5's favour. The old reading of this pair — that the mid-tier matches the flagship at practical depth and only trails at the extreme setting — is the opposite of what v4.3.2 shows.

The cheaper model costs more per finished job. Sonnet 5 lists at $2/$10 against GPT-5.5's $5/$30, a 3x gap on output. Yet AA measured GPT-5.5 as cheaper per completed index task at every tier: $2.63 against $5.09 at the top, $1.54 against $1.79 at high, $0.90 against $1.00 at medium. The reason is verbosity. AA found Sonnet 5 burns roughly 40% more output tokens per index task than Sonnet 4.6 and runs about 3x the agentic turns on knowledge-work evals, and output tokens are where the bill lands. A 3x cheaper rate card does not survive 3x the tokens.

One caveat on how far to carry that. AA's cost figures are measured inside its own harness on its own benchmark basket, where tasks are open-ended enough to let a verbose model run long. On tightly scoped work with capped effort and capped turns, Sonnet 5's rate-card advantage reasserts itself — which is exactly why the recommendation below is about workload shape, not about the leaderboard.

Also note that AA rebased this index from v4.1.1 to v4.3.2. On a rebase every score on the board moves, because the basket of benchmarks changed rather than the models; there is no conversion factor. If you have seen Sonnet 5 quoted at 53 or GPT-5.5 in the mid-50s, those are v4.1.1-era figures and do not translate.

How does pricing compare between Claude Sonnet 5 and GPT-5.5?

On the rate card the two diverge sharply. Sonnet 5 is $2 input / $10 output per million tokens, and that is now permanent — Anthropic's pricing page confirms the $2/$10 launch rate "is now the standard price" and that the increase to $3/$15 scheduled for 1 September 2026 "will not occur". GPT-5.5 is $5 input / $30 output per million — 2.5x on input and 3x on output, where most agentic spend lands.

Two important caveats:

• GPT-5.5 has cheap cached input (around $0.50 per million), so workloads with large, repeated prompt prefixes narrow the gap on the input side.
• Sonnet 5 "works harder," and on open-ended work that reverses the saving. It generates more output tokens and runs more loops per task than a comparable single-shot run — AA measured ~40% more output tokens per index task than Sonnet 4.6 and roughly 3x the agentic turns on knowledge-work evals. On AA's own runs that was enough to make GPT-5.5 the cheaper model per completed task at every effort tier, despite the 3x output rate. Cap effort and cap turns on unbounded tasks, or the cheap rate card stops being cheap.

Both models offer roughly a 1-million-token context window. Note that GPT-5.5 applies a long-context surcharge (higher input and output rates) once a prompt exceeds 272K tokens, so very large contexts cost more on the OpenAI side.

Agentic workhorse vs reasoning flagship: what does that mean in practice?

The labels translate directly into behavior:

• Sonnet 5 (agentic) is happiest orchestrating: reading a repo, editing files, running tests, calling APIs, and looping until the job is done. It is the model you point at a task and let run.
• GPT-5.5 (reasoning) is happiest thinking: given a hard, well-specified problem, it produces a strong, thorough single answer. It is the model you ask a difficult question.

Most production systems need both shapes of work — which is why the practical answer is often "route between them," not "pick one forever."

When should you use Claude Sonnet 5?

• Coding agents and dev automation — multi-file changes, test loops, CI-style iteration where volume and cost matter.
• Tool-heavy pipelines — anything that chains API calls, database queries, and multi-step actions.
• High-throughput work with bounded tasks — Sonnet 5's 3x cheaper output rate is a large advantage when each task is well-scoped and you cap effort, so verbosity cannot eat the discount.
• Large-context jobs that stay under 272K tokens, where you avoid any long-context surcharge and keep costs flat.

When should you use GPT-5.5?

• Reasoning quality at any given effort setting — GPT-5.5 is ~5 index points ahead of Sonnet 5 at high effort and ~6 at medium on v4.3.2, and matches it at the ceiling. If you are not pinning max effort, this is the stronger model.
• One high-stakes answer — dense analysis, research-grade questions, complex decisions where you want the strongest possible response and cost is secondary.
• Heavy cached-prefix workloads — where GPT-5.5's cheap cached input ($0.50/M) offsets its higher headline rate.
• Per-job budgeting — if you bill by the finished task rather than by the token, AA's per-task figures favour GPT-5.5 at every tier.
• Existing OpenAI stack — if your tooling, evals, and infra already live in the OpenAI ecosystem, the switching cost matters.

The verdict: Claude Sonnet 5 or GPT-5.5?

Pick by workload shape, not by rate card:

• Choose Sonnet 5 for agentic, tool-heavy, high-volume work where each task is well-scoped and you control the effort setting — coding agents, automation pipelines, anything you run at scale and can cap. Its advantages are the 3x cheaper output rate, the agentic tuning, and predictable per-token billing.
• Choose GPT-5.5 if you want the better score at a given effort level, or if you budget per finished job rather than per token. On v4.3.2 it leads at matched tiers and came out cheaper per index task at all of them.
• Or run both behind a router: Sonnet 5 for bounded, tool-heavy volume; GPT-5.5 for open-ended reasoning where you would otherwise pin Sonnet 5 to max and pay for the verbosity.

The honest answer in October 2026, though, is that this is no longer the live comparison. Anthropic now lists Claude Sonnet 5 under “Legacy models (still available)” and ships Claude Sonnet 5.5 in its place at the identical $2/$10. Sonnet 5.5 settles the argument: 56.00 at max effort on Intelligence Index v4.3.2 against GPT-5.5's 38.36, and 46.75 at high effort for $1.12 per task against GPT-5.5's 36.98 at $1.54. It is both stronger and cheaper per task than the flagship Sonnet 5 could only draw with. If you are choosing today rather than auditing a running system, start there.

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Frequently asked questions

Is Claude Sonnet 5 better than GPT-5.5?

No, on measured intelligence. On Artificial Analysis's Intelligence Index v4.3.2 (October 2026) they tie at their top tiers — Sonnet 5 (Max) 38.16, GPT-5.5 (Xhigh) 38.36 — but GPT-5.5 leads by about 5 points at high effort and 6 at medium, and AA measured it as cheaper per completed task at every tier ($2.63 vs $5.09 at the top) because Sonnet 5 is much more verbose. Sonnet 5's real advantages are its $2/$10 per-token price, its speed, and its agentic tuning on bounded tasks.

How much cheaper is Claude Sonnet 5 than GPT-5.5?

Per token, 2.5x cheaper on input and 3x on output: $2/$10 against GPT-5.5's $5/$30. The $2/$10 rate is permanent — the increase to $3/$15 once scheduled for September 2026 was cancelled. Per completed task it is not cheaper at all: Artificial Analysis measured Sonnet 5 at $5.09 per index task at max effort against GPT-5.5's $2.63 at xhigh, because Sonnet 5 generates far more output tokens. GPT-5.5's cheap cached input (~$0.50/M) narrows the input gap further on repeated-prefix workloads.

Why is Claude Sonnet 5's index score lower than it used to be?

Because Artificial Analysis rebased its Intelligence Index from v4.1.1 to v4.3.2. On a rebase every score on the board moves, since the basket of benchmarks changed rather than the models. There is no conversion factor between versions, so a v4.1.1 figure of 53 and a v4.3.2 figure of 38.16 describe the same model on two different scales. Every AA number on this page is v4.3.2 as of 5 October 2026.

Do Claude Sonnet 5 and GPT-5.5 have the same context window?

Both offer roughly a 1-million-token context window. GPT-5.5 applies a long-context surcharge on prompts above 272K tokens, so very large contexts cost more on the OpenAI side.

Which model is better for coding agents?

For most coding agents — multi-file edits, running tests, iterating on failures at volume — Claude Sonnet 5 is the stronger practical pick, thanks to its agentic tuning and much lower output pricing. Reserve GPT-5.5 for unusually hard algorithmic or design problems where peak reasoning matters more than throughput.

What does "agentic mid-tier" mean for Claude Sonnet 5?

It means Sonnet 5 is tuned to take many small steps — calling tools, running loops, and iterating — rather than producing one large single answer. In the lineup it shipped into, it sat below the Opus tier but was optimised for the tool-use and automation work agents actually do. As of October 2026 both Sonnet 5 and Opus 4.8 are listed as legacy models; the current equivalents are Claude Sonnet 5.5 and Claude Opus 5.5.

Should I compare Sonnet 5 to Opus 4.8 as well?

Only if you are auditing an existing deployment. Within the lineup these two shipped into, Opus 4.8 was the accuracy ceiling and Sonnet 5 the agentic mid-tier — see our Claude Sonnet 5 vs Claude Opus 4.8 comparison. For a decision you are making now, both are legacy: compare Claude Sonnet 5.5 (56.00 on AA v4.3.2 at max) against Claude Opus 5.5 (57.62) instead.