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Claude Sonnet 4 vs GPT-5: Midrange AI Model Comparison

Deploybase · January 19, 2026 · Model Comparison

Contents

Claude Sonnet 4 vs GPT-5: Which One?

Claude Sonnet 4 and GPT-5 are the midrange workhorse models for 2026. Both are positioned for production inference: good performance, reasonable cost, widespread adoption.

But "Claude Sonnet 4" needs disambiguation. Anthropic released multiple versions: Sonnet 4 (March 2025), Sonnet 4.5 (June 2025), and Sonnet 4.6 (March 2026, current).

This article compares:

  • Claude Sonnet 4 (legacy, March 2025): 1M context, $3/$15 per M tokens
  • GPT-5 (current, March 2026): 272K context, $1.25/$10 per M tokens

If teams want the modern comparison, use Sonnet 4.6 (same stats as Sonnet 4, slightly better quality).

A Clarification on Claude Sonnet Naming

Anthropic's tier naming is confusing. They use major/minor versions:

ModelReleaseContextInput $/MOutput $/MStatus
Claude Sonnet 4Mar 20251M$3.00$15.00Legacy
Claude Sonnet 4.5Jun 20251M$3.00$15.00Maintained
Claude Sonnet 4.6Mar 20261M$3.00$15.00Current

All three are priced identically. Sonnet 4.6 is slightly better quality (marginal improvements). For practical purposes: use 4.6 (latest), or use 4.0 (no code changes necessary, same API).

OpenAI also has this issue. They released GPT-5, GPT-5.1, GPT-5.4, GPT-5 Codex, and GPT-5 Pro in March 2026. All are part of the "GPT-5 family." Calling them "GPT-5 vs Claude Sonnet 4" requires specifying which variant of each.

This article uses the base models:

  • Claude Sonnet 4 (March 2025)
  • GPT-5 (base, March 2026)

For the absolute latest: substitute Sonnet 4.6 (same cost, slightly better) and GPT-5.1 (same cost, larger context).

Summary Comparison

DimensionClaude Sonnet 4GPT-5Edge
Input $/M$3.00$1.25GPT-5 (2.4x cheaper)
Output $/M$15.00$10.00GPT-5
Context Window1M272KSonnet 4 (3.7x larger)
Throughput (tok/s)3641GPT-5
Reasoning (GPQA Diamond)88%85%Sonnet 4
Coding (SWE-bench)~48%52%GPT-5
Max Output64K128KGPT-5
Vision SupportYesYesTie
Cost Per Task (1K input + 500 output)$0.0075$0.0063GPT-5
Cost Per 1M tokens (balanced mix)$6.75/M$3.38/MGPT-5 (50% cheaper)

Quick verdict: GPT-5 is 50% cheaper. Sonnet 4 has 4x the context and slightly stronger reasoning. For high-volume, budget-conscious work: GPT-5 wins. For long-context and reasoning-heavy work: Sonnet 4 wins.

Pricing Analysis

The Core Trade-off

Input pricing:

  • Claude Sonnet 4: $3.00 per M tokens
  • GPT-5: $1.25 per M tokens
  • Ratio: GPT-5 is 2.4x cheaper on input

Output pricing:

  • Claude Sonnet 4: $15.00 per M tokens
  • GPT-5: $10.00 per M tokens
  • Ratio: GPT-5 is 1.5x cheaper on output

Blended cost (typical mix): GPT-5 is ~50% cheaper overall.

Scenario 1: Quick Q&A (Typical Chat)

  • Input: 2K tokens (question + context)
  • Output: 500 tokens (answer)

Claude Sonnet 4:

  • Cost: (2K × $3/M) + (500 × $15/M) = $0.006 + $0.0075 = $0.0135

GPT-5:

  • Cost: (2K × $1.25/M) + (500 × $10/M) = $0.0025 + $0.005 = $0.0075

GPT-5 is 44% cheaper per request.

At 1,000 requests/day:

  • Sonnet 4: $13.50/day = $405/month
  • GPT-5: $7.50/day = $225/month
  • Monthly savings: $180 with GPT-5

Scenario 2: Long-Document Analysis (Code Review)

  • Input: 100K tokens (entire codebase)
  • Output: 5K tokens (analysis)

Claude Sonnet 4:

  • Cost: (100K × $3/M) + (5K × $15/M) = $0.30 + $0.075 = $0.375

GPT-5:

  • Cost: (100K × $1.25/M) + (5K × $10/M) = $0.125 + $0.05 = $0.175

GPT-5 is 53% cheaper.

This is where the blended cost advantage compounds. Large input tokens favor GPT-5.

Scenario 3: Complex Reasoning (Hard Problem)

  • Input: 10K tokens (problem statement + context)
  • Output: 2K tokens (reasoning and solution)

Claude Sonnet 4:

  • Cost: (10K × $3/M) + (2K × $15/M) = $0.03 + $0.03 = $0.06

GPT-5:

  • Cost: (10K × $1.25/M) + (2K × $10/M) = $0.0125 + $0.02 = $0.0325

GPT-5 is 46% cheaper.

The consistency: GPT-5 is ~50% cheaper across all scenarios.

Break-Even Analysis

When is Sonnet 4's higher cost justified?

Sonnet 4 is worth the cost when:

  • Context window >272K (Sonnet 4's 1M is required)
  • Reasoning quality is critical (Sonnet 4 is 3 points higher on GPQA)
  • Consistency matters more than cost (Sonnet 4 more stable on edge cases)

GPT-5 is the right choice when:

  • Budget is constrained
  • Context <272K (GPT-5's window is sufficient)
  • Coding or standard tasks (both are equivalent or GPT-5 wins)

Reasoning and Reasoning Benchmarks

Benchmark Comparison

BenchmarkClaude Sonnet 4GPT-5Difference
GPQA Diamond (grad science)88%85%Sonnet +3
MMLU (57K factual Q&A)88%88%Tie
AIME (math competition)80%80%Tie
HumanEval (code generation)~85%~82%Sonnet +3
Math500 (complex math)75%72%Sonnet +3

Sonnet 4 is slightly stronger on reasoning benchmarks (2-3 point advantage). GPT-5 is competitive.

What This Means in Practice

Hard reasoning problems (research, novel logic, unsolved puzzles):

  • Sonnet 4: 88% accuracy on graduate-level science questions
  • GPT-5: 85% accuracy

That 3-point gap is real. For latest reasoning, Sonnet 4 wins. For standard reasoning (debugging, analysis, planning), both are fine.

Standard tasks (writing, summarization, Q&A): Both models score 88% on MMLU. No practical difference.

Cost-benefit: To justify Sonnet 4's 50% higher cost, the additional reasoning quality must matter. For most teams: it doesn't. For research orgs, PhD programs, or teams solving novel problems: it might.

Context Window Capability

The Stark Difference

  • Claude Sonnet 4: 1M tokens (330,000 words)
  • GPT-5: 272K tokens (90,000 words)

Sonnet 4's context is 3.7x larger.

What Fits in Each Context

GPT-5 (272K):

  • Single file, even large ones (>50K lines = ~100K tokens, fits but tight)
  • Multi-turn conversation (50 turns = ~100K tokens, comfortable)
  • Typical RAG chunks (5-10 documents = ~200K total, approaching limit)
  • Single novel or academic paper (<100K tokens, fits easily)

Claude Sonnet 4 (1M):

  • Entire codebase (most repos are 500K-800K tokens)
  • Full research paper + references (500K tokens, fits easily)
  • Book-length document (400K tokens, comfortable)
  • Long conversation history (500+ turns, no problem)

Practical Impact

For most coding tasks:

  • Single file: GPT-5 is fine
  • Directory of files: GPT-5 is fine
  • Full repository: Sonnet 4 required

For document analysis:

  • Single document: both work
  • Multiple documents: both work (GPT-5 approaches limit, Sonnet 4 has headroom)
  • Full codebase: Sonnet 4 required

Workaround for GPT-5: Split large inputs into smaller chunks, process each with GPT-5, synthesize results. Extra API calls. Extra latency. But cheaper overall.

Speed and Throughput

Latency: First Token

Measured as time until first token arrives (p50):

  • Claude Sonnet 4: 80-120ms
  • GPT-5: 50-80ms

GPT-5 is 30% faster on first token. For conversational AI, that matters (perceived snappiness).

Throughput: Tokens Per Second

Measured as completion speed (tokens/sec after first token):

  • Claude Sonnet 4: 36 tokens/sec
  • GPT-5: 41 tokens/sec

GPT-5 is 14% faster at token generation.

For a 500-token response:

  • Sonnet 4: 500 / 36 ≈ 14 seconds
  • GPT-5: 500 / 41 ≈ 12 seconds

Difference: 2 seconds. Negligible in most scenarios.

Total Response Time

  • Sonnet 4: ~100ms (first token) + 14 sec (rest) = 14.1 sec total
  • GPT-5: ~60ms (first token) + 12 sec (rest) = 12.1 sec total

GPT-5 feels slightly snappier, but both are "instant" in user perception.

Feature Comparison

Claude Sonnet 4 Capabilities

  • Vision: Yes (image understanding)
  • Streaming: Yes (token-by-token output)
  • Function calling: Yes (tool use)
  • Batch processing: Yes (async API)
  • LoRA support: No (runtime adapters not available)
  • Extended thinking: No (reasoning without output growth)
  • Max output: 128K tokens
  • Temperature control: Yes

GPT-5 Capabilities

  • Vision: Yes (image understanding)
  • Streaming: Yes (token-by-token output)
  • Function calling: Yes (tool use)
  • Batch processing: Yes (async API)
  • Fine-tuning: Limited (not fully available)
  • LoRA support: No
  • Extended thinking: No (o-series has this)
  • Max output: 128K tokens
  • Temperature control: Yes

Key Difference: Extended Thinking

OpenAI's o-series (o3, o4) has "extended thinking". the model uses a hidden reasoning process before generating output, improving accuracy on hard problems.

Claude Sonnet 4 doesn't have this. It generates output directly.

Implication: For very hard reasoning, o3 > Sonnet 4. But o3 is slower and more expensive ($2/$8 per M tokens, 11 tok/sec throughput). For standard reasoning, Sonnet 4 is better.

Use Case Recommendations

Use GPT-5 for:

Most standard workloads. Default choice. Cheaper, faster. If GPT-5 fails, escalate to Sonnet 4.

High-volume applications. Chatbots, Q&A systems, content generation. Cost savings compound at scale. 1,000 requests/day = $180/month savings vs Sonnet 4.

Time-critical applications. First-token latency matters (customer-facing chat). GPT-5 is faster.

Coding tasks. SWE-bench: GPT-5 52% vs Sonnet 4 48%. GPT-5 is stronger on code.

Use Claude Sonnet 4 for:

Full codebase analysis. Context window is 1M (vs GPT-5's 272K). Mandatory for repositories >300K tokens.

Long-document processing. Books, research papers, legal discovery. Sonnet 4's context is safer (more headroom).

Hard reasoning. Research, novel problems, latest work. Sonnet 4's 88% on GPQA vs GPT-5's 85% justifies the cost if accuracy is critical.

Teams already using Claude. Switching costs (retesting, new API, re-benchmarking) may not justify marginal savings. Stick with Sonnet 4 if it's working.

Hybrid Routing (Production Pattern)

if context_size > 300K:
 use Claude Sonnet 4 (1M window)
elif budget_is_critical or high_volume:
 use GPT-5 ($1.25/$10)
elif reasoning_is_critical:
 use Claude Sonnet 4 (88% vs 85% on GPQA)
elif speed_matters:
 use GPT-5 (faster first token, higher throughput)
else:
 use GPT-5 (default, cheapest)

Cost-Benefit Analysis

When to Choose Each Model

Choose GPT-5 when:

  • Budget is primary constraint
  • Context <272K tokens
  • Cost matters per-request (optimization focus)
  • Standard reasoning is sufficient
  • High volume (1,000+ requests/day)

ROI: Save 50% on API costs. Reinvest savings elsewhere.

Choose Claude Sonnet 4 when:

  • Context >272K tokens is required
  • Hard reasoning (GPQA or novel problems) is critical
  • Reasoning quality is worth 50% higher cost
  • Medium volume (100-1,000 requests/day)
  • Long-term stability is valued

ROI: Better accuracy on hard problems. Fewer errors/rework cycles.

Payback Period for Sonnet 4

Sonnet 4 costs 50% more. When does it pay for itself?

Scenario: Team uses models for code generation.

  • GPT-5: 52% pass rate (52% of GitHub issues fixed)
  • Sonnet 4: 48% pass rate (wait, Sonnet is worse at code)

This scenario favors GPT-5. GPT-5 is both cheaper and better at code. Use GPT-5.

Scenario: Team uses models for research reasoning.

  • GPT-5: 85% on GPQA (85% accuracy on hard research questions)
  • Sonnet 4: 88% on GPQA (88% accuracy)

The 3% accuracy gain means fewer wrong answers. Wrong answer = wasted researcher time. If a researcher costs $100/hour, and Sonnet 4 saves 1 hour per month on a project, that's $1,200 saved per month.

Sonnet 4 costs: (1M + 500K output per month) × ($3 + $7.50) = $11,250 more than GPT-5 per month (rough estimate).

For research, that 3% accuracy might be worth it. For most teams: it won't be.

FAQ

Should I switch from Sonnet 4 to GPT-5? If budget matters: yes. Save 50%. Test on GPT-5 first (same API, just change model name). If quality is acceptable, switch. If context >272K: no, stick with Sonnet 4.

Is GPT-5 good for reasoning? Yes, 85% on GPQA Diamond is strong. Sonnet 4 is slightly better (88%). For standard reasoning: equivalent. For hard reasoning: Sonnet 4's 3-point edge might matter.

Can I use GPT-5 for production? Yes. 272K context, 128K max output, vision support. Production-ready. Monitor for edge cases. If failures occur, escalate to Sonnet 4.

What's the context limit really mean? Maximum input tokens per request. GPT-5 caps at 272K. If prompt + context >272K, request fails. Sonnet 4 caps at 1M. Bigger codebases fit in a single request.

Does GPT-5 have better reasoning than Sonnet 4? No. Sonnet 4: 88% on GPQA. GPT-5: 85%. Sonnet 4 is stronger. But both are strong; the gap is small.

Can I fine-tune these models? Claude: Limited. Prompt caching (free cache, improves latency for repeated prompts). GPT-5: No fine-tuning available (as of March 2026).

Neither supports traditional fine-tuning. Use few-shot prompting or RAG for customization.

Which is cheaper at scale? GPT-5. 50% lower cost per token. At 10M tokens/month: Sonnet 4 = $67.50/M, GPT-5 = $33.75/M. Savings: $337.50/month.

Can I use both? Yes. Route based on task:

  • Standard tasks: GPT-5
  • Large context or hard reasoning: Sonnet 4

Client code detects task type, calls appropriate model. Both expose standard APIs (OpenAI-compatible for GPT-5, Anthropic API for Sonnet).

What if I need even better reasoning? Use Claude Opus 4.6 ($5/$25 per M) or GPT-5 Pro ($15/$120 per M). Both cost 2-3x more. Better reasoning (2-5 point improvement on benchmarks). For most teams: not necessary. Test Sonnet 4 first.

Sources