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OPTERA LABS

GPT-5.2 Chat VS Claude 3.7 Sonnet

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-5.2 Chat

gpt-5-2

Intelligence Score96%
Cost / 1M Tokens$5.43

70% in · 30% out mix

Value Index(score÷cost)
17.7

Higher = better value

Speed

85/100

Context

256K

Tier

smart

Model B · Anthropic

Claude 3.7 Sonnet

claude-3-7-sonnet

Intelligence Score95%
Cost / 1M Tokens$6.60

70% in · 30% out mix

Value Index(score÷cost)
14.4

Higher = better value

Speed

88/100

Context

200K

Tier

smart

IN-DEPTH ANALYSIS

GPT-5.2 Chat vs Claude 3.7 Sonnet: Detailed Comparison

GPT-5.2 Chat is OpenAI's mid-range-tier language model with a 256K-token context window, excelling at reasoning. Claude 3.7 Sonnet from Anthropic is a mid-range-tier model supporting 200K tokens in context, with standout performance in coding.

This is a genuine tradeoff rather than a clear win. Claude 3.7 Sonnet leads by 2 points on combined coding and reasoning, and charges 18% more per blended million tokens to do it. The margin is narrow enough that the answer depends on your workload: on tasks where the extra capability shows up, the premium pays for itself; on routine work it does not. GPT-5.2 Chat is priced at $1.75/M input tokens and $14.00/M output tokens. Claude 3.7 Sonnet costs $3.00/M input and $15.00/M output.

In independent benchmark evaluations, Claude 3.7 Sonnet leads with coding scores of 97/100 and reasoning scores of 95/100, compared to GPT-5.2 Chat's 94/100 in coding and 96/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-5.2 Chat and Claude 3.7 Sonnet stack up head to head:

coding
94
97
reasoning
96
95
data extraction
91
91
creative tasks
95
92
vision/multimodal
92
90

Best model by task

  • coding: Claude 3.7 Sonnet wins with 97/100
  • reasoning: GPT-5.2 Chat wins with 96/100
  • creative tasks: GPT-5.2 Chat wins with 95/100
  • vision/multimodal: GPT-5.2 Chat wins with 92/100

Estimated monthly cost at scale

At 10M + 2M per month, GPT-5.2 Chat runs about $45.50 while Claude 3.7 Sonnet runs about $60.00 — GPT-5.2 Chat saves roughly $14.50 (24%) every month.

What actually decides it

GPT-5.2 Chat and Claude 3.7 Sonnet come from different labs, which means different tokenizers, different API shapes, and a second vendor relationship. The same English text does not produce the same token count on both, so a price-per-million comparison understates the difference — measure your own prompts on each before treating the headline rates as the full story.

GPT-5.2 Chat and Claude 3.7 Sonnet were released within 11 months of each other, so they are competing on the same evaluations under roughly the same conditions. That makes a direct benchmark comparison meaningful here in a way it usually is not — neither model has the advantage of being measured on a newer, easier set of tests.

Context is effectively a tie: 256K against 200K. Neither model unlocks a document size the other cannot handle, so this axis should not enter the decision. If you were hoping context would break the tie for you, it will not.

Throughput is close enough to ignore — 88/100 versus 85/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

Neither model is the obvious answer. Claude 3.7 Sonnet leads on benchmarks, GPT-5.2 Chat on cost, and the gap is small on both. Run the calculator above with your real token mix — for most workloads that decides it faster than any benchmark table will.

Benchmark Comparison

Head-to-head scores across 5 categories — sourced from official evals

CategoryGPT-5.2 ChatClaude 3.7Winner

Coding

94
97
B

Reasoning

96
95
A

Extraction

91
91
Tied

Creative

95
92
A

Vision

92
90
A
GPT-5.2 Chat: 3 wins
Claude 3.7 Sonnet: 1 wins
GPT-5.2 Chat leads overall

Speed Score

85/100vs88/100
GPT-5.2Claude

Context Window

256Kvs200K
GPT-5.2Claude

What Is a Token?

Models don't read words — they process tokens.

A token is roughly 4 characters of English text (~¾ of a word). Your API bill is priced per million tokens — understanding this directly reduces your costs.

Short phrase

"Hello, world!"

4 tokens
GPT-5.2 Chat
$0.70
Claude 3.7 Sonnet
$1.27

Business email

One typical email (~200 words)

~270 tokens
GPT-5.2 Chat
$47.25
Claude 3.7 Sonnet
$85.86

Code file

50-line Python script

~400 tokens
GPT-5.2 Chat
$70.00
Claude 3.7 Sonnet
$127.20

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.2 Chat and Claude 3.7 Sonnet, so the number only becomes meaningful at production volume. Input tokens only; add your output volume in the calculator below.

How to check your token usage

response.usage.total_tokens

Every API response includes a usage object. Sum total_tokens across all calls to get your monthly figure, then use the calculator below.

Your Cost Calculator

Enter your actual monthly token usage to see real savings

Quick Presets

30.0M TOKENS
Prompt 70%Completion 30%
CHEAPER

GPT-5.2 Chat

$162.75/mo

$1,953.00/yr

$1.75/M in$14/M out

Claude 3.7 Sonnet

$198.00/mo

$2,376.00/yr

$3/M in$15/M out

Annual Savings

$423.00 saved per year

GPT-5.2 Chat cheaper · $35.25/mo

Deep-Dive Audit — GPT-5.2 Chat & Claude 3.7 Sonnet

SURGICAL AUDIT LABF87DE488

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

$478.404

Without optimization protocols, current model choices will result in $159.468 capital loss per year.

EFFICIENCY SCORE

96%

Deep Logic

This model achieves a 96 benchmark score in this category.

CATEGORY GAP

4 pts

Distance from Leader

Competitive Landscape Analysis

Source: MMLU-Pro + GPQA Diamond (Apr 2026)

Category Champion: Claude Fable 5

According to MMLU-Pro + GPQA Diamond (Apr 2026) data, Claude Fable 5 provides the optimum balance for Deep Logic tasks.

Market Score

%100

Savings Rate

%84

Operational Prescription

  • Implement model cascading to optimize token spend.
  • Analyze complex_reasoning data to leverage local semantic caching.

COST AUDIT PROTOCOL

Overkill Detected

"GPT-5.2 Chat is overpriced for this task type. Claude Fable 5 scores 100 in this category at a fraction of the cost."

Categorical Alternative Opportunity

"Claude Fable 5 leads this category with 100 points according to MMLU-Pro + GPQA Diamond (Apr 2026) data."

Inertia Tax Detected

"85% of traffic can be routed to cheaper models. Fast tier (GPT-5 Nano) and Smart tier (o3-mini) can save $13.29/month."

3-Tier Intelligent Routing Architecture

84% SAVINGS VIA ROUTING
Fast Tier
50%

GPT-5 Nano

IQ Score: 72/100

$18.00/yr

Smart Tier
35%

o3-mini

IQ Score: 97/100

$277.20/yr

Power Tier
15%

DeepSeek R1

IQ Score: 97/100

$59.184/yr

Fast Tier 50%Smart Tier 35%Power Tier 15%

Without tiered routing, you pay the 'Inertia Tax' — routing all traffic to the most expensive model regardless of task complexity. Tiered cascade eliminates $1,913.616/year in avoidable overhead.

Deep Logic — Model Cost / Quality Matrix

Source: MMLU-Pro + GPQA Diamond (Apr 2026)
ModelBenchmarkInput (per M)Output (per M)Annual Cost*Value Index
o3-miniBEST VALUE
97/100
$1.10$4.40$66.00
100/100
GPT-5.2 ChatSELECTED
96/100
$1.75$14.00$189.00
35/100
Claude 3.7 Sonnet
95/100
$3.00$15.00$216.00
30/100
GPT-5.6 Terra
94/100
$2.00$12.00$168.00
38/100
Claude 3.5 Sonnet
93/100
$3.00$15.00$216.00
29/100
GPT-4.1
93/100
$2.00$8.00$120.00
53/100
Claude Sonnet 5
92/100
$3.00$15.00$216.00
29/100
Grok 4.5
90/100
$2.00$6.00$96.00
64/100
GPT-4o
90/100
$2.50$10.00$150.00
41/100
Gemini 3.1 Pro
89/100
$2.00$12.00$168.00
36/100
Gemini 2.0 Pro
88/100
$1.25$5.00$75.00
80/100
Gemini 1.5 Pro
87/100
$1.25$5.00$75.00
79/100
Mistral Large 2
86/100
$2.00$6.00$96.00
61/100

* Annual cost for given volumes. Value Index = Score / Cost (Higher = Best Value).

Tactical Code Gen
// iOPTERA Surgical Routing Wrapper
const auditModel = async (prompt: string) => {
  const complexity = measureComplexity(prompt);
  
  // Tactical Cascade Logic
  if (complexity < 0.45) {
    // Redirect simple tasks to efficient model
    return await llm.call("iOPTERA Optimization", prompt); 
  }
  
  // High-latency routing for complex reasoning
  return await llm.call("Claude Fable 5", prompt);
};
READY TO DEPLOY IN Vercel Edge OR AWS Lambda

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