Claude Sonnet 4.6 vs GPT-5.5
| Signal | Claude Sonnet 4.6 | Delta | GPT-5.5 |
|---|---|---|---|
Capabilities | 100 | -- | |
Benchmarks | 82 | -10 | |
Pricing | 85 | +15 | |
Context window size | 95 | 0 | |
Recency | 87 | -12 | |
Output Capacity | 82 | -- | |
| Overall Result | 1 wins | of 6 | 3 wins |
Score History
85.2
current score
GPT-5.5
right now
92.7
current score
Claude Sonnet 4.6
Anthropic
GPT-5.5
OpenAI
Claude Sonnet 4.6 saves you $950.00/month
That's $11400.00/year compared to GPT-5.5 at your current usage level of 100K calls/month.
| Metric | Claude Sonnet 4.6 | GPT-5.5 | Winner |
|---|---|---|---|
| Overall Score | 85 | 93 | GPT-5.5 |
| Rank | #63 | #11 | GPT-5.5 |
| Quality Rank | #63 | #11 | GPT-5.5 |
| Adoption Rank | #63 | #11 | GPT-5.5 |
| Parameters | -- | -- | -- |
| Context Window | 1000K | 1050K | GPT-5.5 |
| Pricing | $3.00/$15.00/M | $5.00/$30.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 100 | Claude Sonnet 4.6 |
| Benchmarks | 82 | 93 | GPT-5.5 |
| Pricing | 85 | 70 | Claude Sonnet 4.6 |
| Context window size | 95 | 96 | GPT-5.5 |
| Recency | 87 | 99 | GPT-5.5 |
| Output Capacity | 82 | 82 | Claude Sonnet 4.6 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 85/100 (rank #63), placing it in the top 79% of all 290 models tracked.
Scores 93/100 (rank #11), placing it in the top 97% of all 290 models tracked.
GPT-5.5 has a 8-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Choose Claude Sonnet 4.6 when you need:
- High-volume production workloads where API costs must be minimized
- Step-by-step reasoning and chain-of-thought problem solving
Choose GPT-5.5 when you need:
- Step-by-step reasoning and chain-of-thought problem solving
Claude Sonnet 4.6 offers 49% better value per quality point. At 1M tokens/day, you'd spend $270.00/month with Claude Sonnet 4.6 vs $525.00/month with GPT-5.5 - a $255.00 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Claude Sonnet 4.6 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1050K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($15.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (93/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
GPT-5.5 has a moderate advantage with a 7.5-point lead in composite score. It wins on more signal dimensions, but Claude Sonnet 4.6 has specific strengths that could make it the better choice for certain workflows.
By Use Case
Best for Quality
Claude Sonnet 4.6
Marginally better benchmark scores; both are excellent
Best for Cost
Claude Sonnet 4.6
49% lower pricing; better value at scale
Best for Reliability
Claude Sonnet 4.6
Higher uptime and faster response speeds
Best for Prototyping
Claude Sonnet 4.6
Stronger community support and better developer experience
Best for Production
Claude Sonnet 4.6
Wider enterprise adoption and proven at scale
by Anthropic
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 49% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Claude Sonnet 4.6 | GPT-5.5 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Claude Sonnet 4.6
Anthropic
GPT-5.5
OpenAI
Claude Sonnet 4.6 saves you $21.60/month
That's 48% cheaper than GPT-5.5 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Claude Sonnet 4.6 | GPT-5.5 |
|---|---|---|
| Context Window | 1M | 1.1M |
| Max Output Tokens | 128,000 | 128,000 |
| Open Source | No | No |
| Created | Feb 17, 2026 | Apr 24, 2026 |
The single rank difference likely comes from GPT-5.5's slightly larger 1.1M token context window versus Claude's 1.0M, which can matter for large codebases. However, this 10% context advantage comes at a steep price - GPT-5.5 costs 1.67x more for input ($5/M vs $3/M) and exactly 2x more for output ($30/M vs $15/M), making the ranking differential questionable for most coding workflows.
Claude Sonnet 4.6 would cost $60/month ($30 input + $30 output) while GPT-5.5 would run $110/month ($50 input + $60 output), an 83% premium. Since both models share identical capabilities and the same 66/100 performance score, you're essentially paying $50 extra per month for an additional 100K tokens of context that most coding tasks won't utilize.
With matching 128K max output tokens and identical features (vision, function calling, streaming, JSON mode, reasoning, web search), the decision hinges on ecosystem integration and API stability. Anthropic's Claude models typically offer more granular safety controls and better prompt adherence, while OpenAI provides broader third-party tooling support - factors that matter more than the negligible 100K context window difference when both models score identically at 66/100.
The only justifiable scenario would be processing massive monorepos where that extra 100K context (1.1M vs 1.0M) prevents critical code from being truncated, but even then it's marginal. For the vast majority of coding tasks - from PR reviews to test generation - you're paying double the output price ($30/M vs $15/M) for zero performance gain, making GPT-5.5 a poor value proposition at rank #8 versus Claude's #9.
The 100K token difference represents roughly 75,000 words or about 2,500 lines of code - meaningful for analyzing entire microservices but irrelevant for most coding tasks. Since both models already support 128K token outputs and score identically at 66/100, you're unlikely to hit Claude's context limit before hitting quality degradation issues that affect both models equally at extreme context lengths.