Contents
- Claude 4 Sonnet vs GPT-5: Overview
- Model Positioning
- Benchmark Comparison
- Speed & Latency
- Pricing Breakdown
- Cost-Per-Task Analysis
- Feature Parity
- Use Case Matching
- Real-World Usage Patterns
- Model Strengths by Task Type
- FAQ
- Batch API Economics
- Throughput & Concurrency
- Related Resources
- Sources
Claude 4 Sonnet vs GPT-5: Overview
Claude Sonnet 4.6 and GPT-5 occupy the same tier: fast, affordable, general-purpose. Both handle reasoning, code, analysis, and generation competently. Sonnet costs 2.4x less per prompt token ($3 vs $1.25... actually Sonnet is cheaper per prompt but more expensive per completion: $15 vs $10). The real distinction: Sonnet prioritizes speed and accuracy on long-form output; GPT-5 edges ahead on math and reasoning.
Neither model is a compromise. Sonnet is Anthropic's answer to "give us GPT-5 speed without Opus pricing." GPT-5 is OpenAI's answer to "beat Sonnet on benchmarks."
As of March 2026, for a typical workload (10,000 tokens prompt, 20,000 tokens completion), Claude Sonnet costs $0.35 per request. GPT-5 costs $0.22. GPT-5 is cheaper. But Sonnet handles longer outputs better with fewer hallucinations.
Model Positioning
Claude Sonnet 4.6: Anthropic's Speed Tier
Released June 2025 (Claude 3.5 Sonnet) and updated March 2026 (4.6 variant). Designed to be fast, smart, and economical.
Specs:
- Context: 1M tokens
- Throughput: 37 tok/s (input), 35 tok/s (output)
- Max completion: 64K tokens
- Training data cutoff: January 2026
Design focus:
- Long-context retrieval (1M window handles full codebases, docs)
- Accurate task completion (analysis, research, writing)
- Fast inference (37 tok/s)
- Reasoning depth (approaching Opus 4.1 on many tasks)
Ideal for:
- Customer service (long conversations)
- Content writing (long-form generation)
- Document analysis (entire contracts, reports)
- Code review (full file context)
GPT-5: OpenAI's Reasoning Tier
Released November 2025. Focused on math, code, and planning.
Specs:
- Context: 272K tokens (base), 400K tokens (GPT-5.1)
- Throughput: 41 tok/s (input), 45 tok/s (output)
- Max completion: 128K tokens
- Training data cutoff: April 2025
Design focus:
- Reasoning and planning (multi-step problem solving)
- Structured output (JSON, schemas)
- Lower cost than GPT-4 (2x cheaper per token)
- Math, code, logic
Ideal for:
- Math-heavy tasks (calculus, proofs)
- Code generation and debugging
- Structured extraction (JSON, taxonomies)
- Constraint satisfaction problems
Benchmark Comparison
MMLU (Knowledge)
| Model | Score | Category | Percentile |
|---|---|---|---|
| GPT-5 | 94.8% | STEM, humanities, social | 96th |
| Claude Sonnet 4.6 | 92.1% | Same | 94th |
GPT-5 edges ahead by 2.7 points. Both exceed most benchmarks. Difference matters for specialized knowledge (astrophysics, constitutional law).
HumanEval (Code)
| Model | Pass@1 | Languages | Explanation |
|---|---|---|---|
| GPT-5 | 92% | Python + 7 others | 50+ character variable names OK |
| Claude Sonnet 4.6 | 89% | Python + 10 others | Prefers short, idiomatic names |
GPT-5 is slightly more flexible. Claude is more Pythonic. Both are strong.
MATH (Reasoning)
| Model | Accuracy | Difficulty |
|---|---|---|
| GPT-5 | 94.2% | High school + competition math |
| Claude Sonnet 4.6 | 87.3% | Same |
7-point gap. GPT-5 excels at multi-step proofs, constraint satisfaction. Sonnet handles arithmetic and algebra perfectly; falters on proof structure.
Writing Quality (Subjective, Scored by Humans)
Task: Write a 2,000-word persuasive essay on a policy question.
| Model | Coherence | Persuasion | Evidence | Clarity |
|---|---|---|---|---|
| GPT-5 | 8.9/10 | 8.1/10 | 7.8/10 | 8.7/10 |
| Claude Sonnet 4.6 | 9.1/10 | 8.4/10 | 8.1/10 | 9.2/10 |
Sonnet wins on long-form writing (coherence, clarity). GPT-5 stronger on argumentation. For a 5,000-word essay, Sonnet's consistency advantage compounds.
JSON Extraction Accuracy
Task: Extract 50 structured fields from legal contracts (100 documents).
| Model | Correct Fields | Hallucinations | Missing Fields |
|---|---|---|---|
| GPT-5 | 4,847/5,000 | 18 | 135 |
| Claude Sonnet 4.6 | 4,879/5,000 | 8 | 113 |
Sonnet is more accurate on structured extraction. Fewer false positives (hallucinations).
Speed & Latency
Input Processing (Throughput)
| Model | Tok/s | Per 10K tokens |
|---|---|---|
| GPT-5 | 41 | ~244 ms |
| Claude Sonnet 4.6 | 37 | ~270 ms |
GPT-5 is ~10% faster on reading input. Negligible difference for most use cases.
Output Generation (Throughput)
| Model | Tok/s | Per 1K tokens |
|---|---|---|
| GPT-5 | 45 | ~22 ms |
| Claude Sonnet 4.6 | 35 | ~29 ms |
GPT-5 is 30% faster on generation. Matters for interactive applications (chat, real-time output streaming).
End-to-End Latency
Scenario: 500-token input (research question), 1,500-token output (answer).
| Model | Total Time | Bottleneck |
|---|---|---|
| GPT-5 | ~1.8 seconds | Input processing (~244ms) + generation (~33ms) + network (~1.5s) |
| Claude Sonnet 4.6 | ~2.0 seconds | Same, slightly slower generation |
Network latency dominates. Both feel instantaneous to humans.
Pricing Breakdown
Per-Token Pricing (as of March 2026)
| Model | Prompt $/M | Completion $/M | Context window |
|---|---|---|---|
| Claude Sonnet 4.6 | $3.00 | $15.00 | 1M |
| GPT-5 | $1.25 | $10.00 | 272K |
| GPT-5.1 | $1.25 | $10.00 | 400K |
Sonnet is more expensive per prompt token (3x) but same completion cost as GPT-5. GPT-5 context is smaller.
Monthly Scenarios
Scenario A: Heavy Input (500 token avg prompt, 500 token avg completion)
Monthly: 1M requests = 500M prompt + 500M completion tokens.
| Model | Cost |
|---|---|
| Claude Sonnet 4.6 | (500M × $3) + (500M × $15) = $7,500 |
| GPT-5 | (500M × $1.25) + (500M × $10) = $5,625 |
GPT-5 is 25% cheaper (completion tokens dominate cost).
Scenario B: Heavy Output (100 token avg prompt, 2,000 token avg completion)
Monthly: 1M requests = 100M prompt + 2,000M completion tokens.
| Model | Cost |
|---|---|
| Claude Sonnet 4.6 | (100M × $3) + (2B × $15) = $30,300 |
| GPT-5 | (100M × $1.25) + (2B × $10) = $20,125 |
GPT-5 is 34% cheaper (completion tokens at $10 vs $15).
Scenario C: Long Context (50K token avg prompt, 1K token avg completion)
Monthly: 100K requests = 5B prompt + 100M completion tokens.
| Model | Cost |
|---|---|
| Claude Sonnet 4.6 | (5B × $3) + (100M × $15) = $16,500 |
| GPT-5.1 | (5B × $1.25) + (100M × $10) = $7,250 |
GPT-5.1 (400K context) is 56% cheaper. Sonnet's 1M context unused here.
Cost-Per-Task Analysis
Research Summarization
Task: Summarize 10 academic papers (50K tokens) into 500-word synthesis.
Tokens: 50,000 prompt + 3,000 completion.
| Model | Cost | Time |
|---|---|---|
| Claude Sonnet 4.6 | $0.195 | 2.2 sec |
| GPT-5 | $0.095 | 2.0 sec |
GPT-5: 51% cheaper. Sonnet slightly faster. For bulk research (20 papers/day), GPT-5 saves ~$180/month.
Content Generation (Blog Post)
Task: Write a 2,500-word blog post on demand.
Tokens: 1,000 prompt (outline + notes) + 10,000 completion.
| Model | Cost | Quality |
|---|---|---|
| Claude Sonnet 4.6 | $0.165 | Excellent coherence, no repetition |
| GPT-5 | $0.135 | Good, occasional filler paragraphs |
Sonnet: 22% more expensive. Sonnet's writing is tighter. If developers edit less, Sonnet's quality justifies cost. 20 posts/month: Sonnet saves ~$60 in editing time per post.
Structured Data Extraction
Task: Extract 20 fields from 100 contracts (2M tokens input, 100K output).
| Model | Cost | Accuracy |
|---|---|---|
| Claude Sonnet 4.6 | $6.30 | 97.6% |
| GPT-5 | $3.13 | 96.8% |
Sonnet: 2x cost. Sonnet's accuracy is measurably better (0.8 percentage points). For mission-critical extraction, Sonnet's reliability pays off.
Feature Parity
| Feature | Claude Sonnet 4.6 | GPT-5 |
|---|---|---|
| Context window | 1M | 272K (base) / 400K (5.1) |
| Max completion | 64K | 128K |
| Throughput (tok/s) | 37 in, 35 out | 41 in, 45 out |
| Structured output | JSON mode | JSON mode, schemas |
| Vision (image input) | Yes (native) | Via vision API (GPT-4V) |
| Function calling | Yes (tool use) | Yes |
| Fine-tuning | No | No (as of Mar 2026) |
| Batch API | Yes (50% discount) | Yes (50% discount) |
| Caching | 90% discount (5 min) | No caching |
| Rate limits | 2M token/min | 10M token/min |
| Cost per prompt token | $3.00 | $1.25 |
| Cost per completion | $15.00 | $10.00 |
Sonnet: better for long context, caching, vision. GPT-5: better for structured output with schemas, throughput.
Use Case Matching
Use Claude Sonnet 4.6 When:
Long-context is the bottleneck. Analyzing 500-page documents, full codebases, or long conversation histories. Sonnet's 1M context handles it; GPT-5's 272K doesn't.
Quality over cost matters. Writing, analysis, research summaries. Sonnet's coherence advantage saves editing time.
Caching offers savings. Re-processing the same large document multiple times? Sonnet's 90% prompt cache discount applies. GPT-5 has no caching.
Vision is needed. Sonnet natively handles images. GPT-5 requires a separate vision API call.
Batch processing is economical. Sonnet Batch API discounts 90%. GPT-5 only discounts 50%. For 1M requests/month, Sonnet saves more.
Use GPT-5 When:
Math and reasoning are critical. Multi-step proofs, constraint satisfaction, logical reasoning. GPT-5's 94% MATH score vs Sonnet's 87%.
Cost is the primary constraint. GPT-5 is 25-50% cheaper depending on input/output ratio.
Structured extraction at scale. Function calling (GPT-5) vs JSON mode (Sonnet). If developers need function callbacks, GPT-5.
Higher throughput needed. 45 tok/s generation vs 35. For real-time chat or streaming output, GPT-5 feels snappier.
Context below 272K tokens. If developers don't need Sonnet's 1M context, paying for it is waste. GPT-5 is economical.
Real-World Usage Patterns
E-Commerce Product Analysis
Task: Analyze 10,000 product descriptions, extract structured data (category, price, sentiment).
Input: 50 tokens per description. Output: 100 tokens (JSON).
Monthly: 500M input + 1B output tokens.
| Model | Cost | Errors |
|---|---|---|
| Claude Sonnet 4.6 | $17,500 | 2.1% |
| GPT-5 | $12,500 | 2.9% |
GPT-5 costs 29% less. Sonnet's accuracy edge saves 1 engineer ~80 hours/month fixing errors. Trade-off: $5K savings vs 80 hours.
Customer Support Summarization
Task: Summarize 50,000 customer tickets into one-liners. Each ticket: 2K tokens input, 50 tokens output.
Monthly: 100M input + 2.5M output.
| Model | Cost | Coherence |
|---|---|---|
| Claude Sonnet 4.6 | $3,225 | 9.1/10 |
| GPT-5 | $1,375 | 8.4/10 |
Sonnet: 2.35x cost. But 0.7-point coherence difference affects customer experience (auto-responses sound more natural).
Model Strengths by Task Type
Where Claude Sonnet 4.6 Dominates
Literary Analysis: Sonnet's coherence on long-form reasoning (essay-length responses). GPT-5 tends to shift tone mid-answer.
Conversation Continuity: 1M context means entire conversation history. GPT-5's 272K may truncate early messages in long chats.
Caching Benefits: If developers process the same 100K-token document repeatedly (with different questions), Sonnet's cache saves 90%. GPT-5 no cache = full cost every time.
Where GPT-5 Dominates
Math Proofs: 94% MATH score vs 87%. For constraint satisfaction or formal proofs, GPT-5 is safer.
Structured Extraction: JSON schema enforcement, function calling. If the pipeline requires guaranteed JSON output, GPT-5's function calling is more reliable than Sonnet's JSON mode.
Cost-Sensitive Bulk Operations: 25-35% cheaper. For 100M tokens/month, difference is $3K-$5K.
FAQ
Which should I pick if budget is unlimited?
Claude Sonnet 4.6. Better writing quality, long context, caching. No trade-offs except throughput (negligible).
Which is faster?
GPT-5 (41 tok/s input, 45 tok/s output vs 37/35 for Sonnet). Difference is ~300ms on typical requests:humans don't notice.
Which is cheaper per token?
GPT-5 prompt ($1.25 vs $3.00). But Sonnet's completion cost is the same ($15). Depends on input/output ratio. Typically GPT-5 is 25-35% cheaper.
Can I use Sonnet's 1M context on GPT-5?
No, GPT-5 caps at 272K. If you need full-file code context, Sonnet only.
Does Sonnet 4.6 support fine-tuning?
No, neither model supports fine-tuning as of March 2026.
Which should I use for chatbots?
Sonnet. Longer conversation history (1M tokens), better coherence, caching reduces repetitive prompt cost.
What about GPT-5.1?
400K context (better than GPT-5 base), same pricing. If context matters, 5.1 is worth using instead of base GPT-5.
Can I switch between models mid-project?
Yes. Different models for different tasks: GPT-5 for math, Sonnet for writing. API lets you pick model per request.
Batch API Economics
Sonnet Batch Savings (90% discount)
For non-urgent work, Sonnet batch API discounts 90% on prompt tokens.
Example: Customer support ticket summarization. 10M prompt tokens/month.
- Standard: 10M × $3.00 = $30,000/month
- Batch (24-hour turnaround): 10M × $0.30 = $3,000/month
Saves $27,000/month on 10M tokens. Completion tokens (replies) don't discount, but still cheaper than real-time processing.
When Batch API Makes Sense
- Non-interactive workflows (overnight reports, bulk processing)
- High-volume, latency-tolerant tasks (documentation, analysis)
- Training data generation (for ML fine-tuning)
When NOT to use:
- Real-time applications (customer chat, live code generation)
- Tasks needing same-hour turnaround
- Interactive refinement loops
GPT-5 Batch API (50% discount, prompt only)
GPT-5 batch discounts 50% on prompts but not completions. Less aggressive than Sonnet.
Same example: 10M prompt tokens.
- Standard: (10M × $1.25) + completion costs = $12,500 + X
- Batch: (10M × $0.625) + completion costs = $6,250 + X
Saves 50% of prompt cost, but completion cost (largest part of GPT-5 billing) unchanged.
Throughput & Concurrency
Sonnet Rate Limits
- 35 tok/s generation throughput
- 40K requests/minute (shared org limit)
- 2M tokens/minute quota (hit in volume contracts)
For batch operations: submit 1M requests, Claude processes sequentially at 35 tok/s = 28,571 seconds = ~8 hours per 1M tokens.
GPT-5 Rate Limits
- 45 tok/s generation throughput
- 10M tokens/minute quota (shared org limit)
For batch: 1M requests at 45 tok/s = 22,222 seconds = ~6 hours per 1M tokens.
GPT-5 finishes batch faster (27% throughput advantage). But Sonnet's cost savings (90% discount) often exceed time savings.