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OPTERA LABS

GPT-5.6 Terra VS GPT-4.1

2026 Cost & Performance Comparison
Model A · OpenAI

GPT-5.6 Terra

gpt-5-6-terra

Intelligence Score94%
Cost / 1M Tokens$5.00

70% in · 30% out mix

Value Index(score÷cost)
18.8

Higher = better value

Speed

86/100

Context

1.1M

Tier

smart

Model B · OpenAI

GPT-4.1

gpt-4-1

Intelligence Score93%
Cost / 1M Tokens$3.80

70% in · 30% out mix

Value Index(score÷cost)
24.5

Higher = better value

Speed

88/100

Context

1.0M

Tier

smart

IN-DEPTH ANALYSIS

GPT-5.6 Terra vs GPT-4.1: Detailed Comparison

GPT-5.6 Terra is OpenAI's mid-range-tier language model with a 1.1M-token context window, excelling at reasoning. GPT-4.1 from OpenAI is a mid-range-tier model supporting 1.0M tokens in context, with standout performance in reasoning.

This is a genuine tradeoff rather than a clear win. GPT-5.6 Terra leads by 3 points on combined coding and reasoning, and charges 24% 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-5.6 Terra is priced at $2.00/M input tokens and $12.00/M output tokens. GPT-4.1 costs $2.00/M input and $8.00/M output.

In independent benchmark evaluations, GPT-5.6 Terra leads with coding scores of 93/100 and reasoning scores of 94/100, compared to GPT-4.1's 91/100 in coding and 93/100 in reasoning.

Capability breakdown

Across the five core benchmark categories, here is how GPT-5.6 Terra and GPT-4.1 stack up head to head:

coding
93
91
reasoning
94
93
data extraction
93
92
creative tasks
92
90
vision/multimodal
93
91

Best model by task

  • coding: GPT-5.6 Terra wins with 93/100
  • reasoning: GPT-5.6 Terra wins with 94/100
  • data extraction: GPT-5.6 Terra wins with 93/100
  • creative tasks: GPT-5.6 Terra wins with 92/100
  • vision/multimodal: GPT-5.6 Terra wins with 93/100

Estimated monthly cost at scale

At 10M + 2M per month, GPT-5.6 Terra runs about $44.00 while GPT-4.1 runs about $36.00 — GPT-4.1 saves roughly $8.00 (18%) every month.

What actually decides it

Both models come from OpenAI, so they share a tokenizer, an API surface, and a billing account. Switching between them is a model-string change and nothing else — no new SDK, no re-tokenizing your prompts to re-estimate cost, no second vendor to onboard. That makes routing between them far cheaper to implement than a cross-vendor split.

These are the same lab's model at the same tier, 15 months apart — so this is an upgrade question, not a choice between alternatives. GPT-5.6 Terra is the current entry; GPT-4.1 is here because plenty of production traffic still runs on it. If you are starting something new there is little reason to pick the older one, and if you are already on it the question is whether the migration cost is worth the gain.

Context is effectively a tie: 1.1M against 1.0M. 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 86/100. 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. GPT-5.6 Terra leads on benchmarks, GPT-4.1 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-5.6 TerraGPT-4.1Winner

Coding

93
91
A

Reasoning

94
93
A

Extraction

93
92
A

Creative

92
90
A

Vision

93
91
A
GPT-5.6 Terra: 5 wins
GPT-4.1: 0 wins
GPT-5.6 Terra leads overall

Speed Score

86/100vs88/100
GPT-5.6GPT-4.1

Context Window

1050Kvs1000K
GPT-5.6GPT-4.1

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 Terra
$0.80
GPT-4.1
$0.80

Business email

One typical email (~200 words)

~270 tokens
GPT-5.6 Terra
$54.00
GPT-4.1
$54.00

Code file

50-line Python script

~400 tokens
GPT-5.6 Terra
$80.00
GPT-4.1
$80.00

Prices shown are for 100,000 runs of each workload — one run costs a fraction of a cent on both GPT-5.6 Terra and GPT-4.1, 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 Terra

$150.00/mo

$1,800.00/yr

$2/M in$12/M out
CHEAPER

GPT-4.1

$114.00/mo

$1,368.00/yr

$2/M in$8/M out

Annual Savings

$432.00 saved per year

GPT-4.1 cheaper · $36.00/mo

Deep-Dive Audit — GPT-5.6 Terra & GPT-4.1

SURGICAL AUDIT LAB39B1214A

Surgically Auditing: Deep Logic

Leakage Detected

3-YEAR STRATEGIC LOSS PROJECTION

$415.404

Without optimization protocols, current model choices will result in $138.468 capital loss per year.

EFFICIENCY SCORE

94%

Deep Logic

This model achieves a 94 benchmark score in this category.

CATEGORY GAP

6 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

%82

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 Terra 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 $11.54/month."

3-Tier Intelligent Routing Architecture

82% 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 $1,661.616/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 Chat
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 TerraSELECTED
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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