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OPTERA LABS

GPT-5.6 Sol VS Claude Opus 4.6

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-5.6 Sol

gpt-5-6-sol

Intelligence Score99%
Cost / 1M Tokens$12.50

70% in · 30% out mix

Value Index(score÷cost)
7.9

Higher = better value

Speed

70/100

Context

1.1M

Tier

power

Model B · Anthropic

Claude Opus 4.6

claude-opus-4-6

Intelligence Score98%
Cost / 1M Tokens$11.00

70% in · 30% out mix

Value Index(score÷cost)
8.9

Higher = better value

Speed

60/100

Context

200K

Tier

power

IN-DEPTH ANALYSIS

GPT-5.6 Sol vs Claude Opus 4.6: Detailed Comparison

GPT-5.6 Sol is OpenAI's flagship-tier language model with a 1.1M-token context window, excelling at reasoning. Claude Opus 4.6 from Anthropic is a flagship-tier model supporting 200K tokens in context, with standout performance in coding.

Price is not the deciding factor here. Claude Opus 4.6 and GPT-5.6 Sol land within 12% of each other on a typical prompt/completion mix, which is inside the margin your own input/output ratio will move anyway. Pick on capability, context, or latency instead — the monthly bill will look much the same either way. GPT-5.6 Sol is priced at $5.00/M input tokens and $30.00/M output tokens. Claude Opus 4.6 costs $5.00/M input and $25.00/M output.

In independent benchmark evaluations, Claude Opus 4.6 leads with coding scores of 100/100 and reasoning scores of 98/100, compared to GPT-5.6 Sol's 97/100 in coding and 99/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-5.6 Sol and Claude Opus 4.6 stack up head to head:

coding
97
100
reasoning
99
98
data extraction
95
95
creative tasks
96
96
vision/multimodal
96
94

Best model by task

  • coding: Claude Opus 4.6 wins with 100/100
  • reasoning: GPT-5.6 Sol wins with 99/100
  • vision/multimodal: GPT-5.6 Sol wins with 96/100

Estimated monthly cost at scale

At 10M + 2M per month, GPT-5.6 Sol runs about $110.00 while Claude Opus 4.6 runs about $100.00 — Claude Opus 4.6 saves roughly $10.00 (9%) every month.

What actually decides it

GPT-5.6 Sol and Claude Opus 4.6 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-5.6 Sol and Claude Opus 4.6 were released within 3 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.

The context gap is the largest single difference on this pair: GPT-5.6 Sol takes 1.1M tokens against 200K for Claude Opus 4.6, roughly 5.3x. That is the difference between feeding in a whole repository or a full contract set and having to chunk it. If your work involves documents you cannot split cleanly, this decides it on its own.

Throughput is close enough to ignore — 70/100 versus 60/100. Neither model will feel noticeably quicker in an interactive product, so latency is not a reason to choose between them.

With prices this close (12% apart), pick on fit rather than cost. Claude Opus 4.6 holds the benchmark edge; weigh that against where each one is weakest — data extraction and vision processing respectively — and use the calculator above with your own token mix.

Benchmark Comparison

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

CategoryGPT-5.6 SolClaude OpusWinner

Coding

97
100
B

Reasoning

99
98
A

Extraction

95
95
Tied

Creative

96
96
Tied

Vision

96
94
A
GPT-5.6 Sol: 2 wins
Claude Opus 4.6: 1 wins
GPT-5.6 Sol leads overall

Speed Score

70/100vs60/100
GPT-5.6Claude

Context Window

1050Kvs200K
GPT-5.6Claude

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-5.6 Sol
$2.00
Claude Opus 4.6
$2.20

Business email

One typical email (~200 words)

~270 tokens
GPT-5.6 Sol
$135.00
Claude Opus 4.6
$148.50

Code file

50-line Python script

~400 tokens
GPT-5.6 Sol
$200.00
Claude Opus 4.6
$220.00

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Sol and Claude Opus 4.6, 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%

GPT-5.6 Sol

$375.00/mo

$4,500.00/yr

$5/M in$30/M out
CHEAPER

Claude Opus 4.6

$330.00/mo

$3,960.00/yr

$5/M in$25/M out

Annual Savings

$540.00 saved per year

Claude Opus 4.6 cheaper · $45.00/mo

Deep-Dive Audit — GPT-5.6 Sol & Claude Opus 4.6

SURGICAL AUDIT LAB6E74807D

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

$1,171.404

Without optimization protocols, current model choices will result in $390.468 capital loss per year.

EFFICIENCY SCORE

99%

Deep Logic

This model achieves a 99 benchmark score in this category.

CATEGORY GAP

1 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

%93

Operational Prescription

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

COST AUDIT PROTOCOL

Overkill Detected

"GPT-5.6 Sol 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 $32.54/month."

3-Tier Intelligent Routing Architecture

93% 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 $4,685.616/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
Claude Fable 5LEADER
100/100
$10.00$50.00$720.00
5/100
GPT-5.6 SolSELECTED
99/100
$5.00$30.00$420.00
8/100
Claude Opus 5
98/100
$5.00$25.00$360.00
9/100
Claude Opus 4.6
98/100
$5.00$25.00$360.00
9/100
DeepSeek R1BEST VALUE
97/100
$0.55$2.19$32.88
100/100
Claude Opus 4.8
96/100
$5.00$25.00$360.00
9/100
Claude 3 Opus
90/100
$15.00$75.00$1,080.00
3/100
Llama 3.1 405B
88/100
$2.70$2.70$64.80
46/100

* 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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