GPT-5.5 vs Claude Sonnet 4.6: Best $5-tier model
GPT-5.5 and Claude Sonnet 4.6 are chasing the same buyer: teams that want serious AI without paying Opus-level rates. The catch is that the two price lists barely line up, so calling either one a "$5 model" hides the part that actually shows up on your bill, what you pay for output.
Here's the short version for anyone running the numbers for a business. Two of the most capable mid-priced AI models on the market right now look almost identical on the spec sheet, and both advertise a headline input price in the $3-to-$5 range. So the obvious question from a finance-minded buyer is: does it matter which one we pick?
It does, but not for the reason the marketing pages push. The gap that counts isn't raw intelligence. The benchmark scores are close enough that you'd struggle to feel the difference day to day. The gap is what each model charges to write its answers back to you. Sonnet 4.6 charges half what GPT-5.5 does per million output tokens, and for anything that produces long replies, a coding assistant, a content tool, a research summariser, output is where the money goes.
A note before the comparison, because it changed the conclusion: some of the figures floating around for these models don't hold up against the official documentation. The context-window numbers in particular were off, and we've corrected and flagged them below. The pricing, which is the part most likely to affect your budget, checks out.
Head-to-head benchmarks
| Metric | GPT-5.5 | Sonnet 4.6 | Delta |
|---|---|---|---|
| SWE-bench Pro | 58.6% (reported) | 58.1% (reported) | +0.5 pts (GPT) |
| MMLU | 88.4% (reported) | 87.6% (reported) | +0.8 pts (GPT) |
| Context window | ~1.05M | 1M | roughly even |
| Price (input) | $5.00 / 1M | $3.00 / 1M | Sonnet 40% cheaper |
| Price (output) | $30.00 / 1M | $15.00 / 1M | Sonnet 50% cheaper |
A caveat on that table. The benchmark scores above circulated widely after launch, but we couldn't tie them back to a primary source from either vendor, so treat them as reported rather than confirmed. For what it's worth, Anthropic's own published numbers put Sonnet 4.6 closer to 79-80% on SWE-bench Verified (Anthropic Sonnet 4.6 benchmarks), which is a different test from the SWE-bench Pro figure quoted here, another reason not to lean too hard on a single percentage.
The pricing reality
Input pricing is in the same neighbourhood ($5 against $3), so on its own it's not decisive. The output side is where they split. GPT-5.5 charges $30 per million output tokens (OpenAI GPT-5.5 model docs); Sonnet 4.6 charges $15 (Anthropic: Introducing Sonnet 4.6). For any tool that writes a lot back, coding assistants, content generation, long-form analysis, that 2x gap on output ends up driving the total.
Take a coding assistant that chews through 1M input tokens and produces 2M output tokens a day:
- GPT-5.5: $5 + $60 = $65/day = $1,950/month
- Sonnet 4.6: $3 + $30 = $33/day = $990/month
For the same work, Sonnet 4.6 lands at close to half the cost. (Real bills can drift from this if long-context premium tiers kick in, so use it as a baseline, not a quote.)
Benchmark context
The capability gap, as reported, is tiny: half a point on SWE-bench Pro, under a point on MMLU. At that margin you won't notice a difference in normal use, and as noted above the underlying numbers aren't confirmed by the vendors. Either model handles coding, analysis, and general Q&A well. If you're choosing between them, the benchmark column isn't where the decision lives.
Context window
This is where the original framing fell apart, and it's worth being straight about. Earlier write-ups, including our own first pass, put GPT-5.5 at a 400K context window, which would have handed Sonnet 4.6 a 600K head start. OpenAI's own documentation says otherwise: GPT-5.5 runs roughly a 1.05M-token context with up to 128K output (OpenAI GPT-5.5 model docs). Sonnet 4.6 sits at 1M (Anthropic Sonnet 4.6), originally described as beta, though later Anthropic announcements suggest 1M moved to general availability at standard pricing, so the "beta" label may be out of date.
The practical takeaway: for codebase analysis, legal document review, and other long-context jobs, the two are effectively level. Neither one forces the kind of document-chunking that the older 400K figure implied for GPT-5.5.
Verdict
Sonnet 4.6 still wins, but on cost, not on context. The performance is close enough to call a draw, the context windows are now comparable, and Sonnet does the same job for roughly half the total spend on output-heavy workloads. If you depend on OpenAI-specific features, custom GPTs, the Assistants API, that can tip the call back the other way. For most teams optimising the bill, Sonnet 4.6 is the sensible pick.