GPT-5.2 Chat
gpt-5-2
70% in · 30% out mix
Higher = better value
Speed
85/100
Context
256K
Tier
smart
GPT-4.1
gpt-4-1
70% in · 30% out mix
Higher = better value
Speed
88/100
Context
1.0M
Tier
smart
IN-DEPTH ANALYSIS
GPT-5.2 Chat vs GPT-4.1: Detailed Comparison
GPT-5.2 Chat is OpenAI's mid-range-tier language model with a 256K-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-5.2 Chat leads by 6 points on combined coding and reasoning, and charges 30% 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. GPT-4.1 costs $2.00/M input and $8.00/M output.
In independent benchmark evaluations, GPT-5.2 Chat leads with coding scores of 94/100 and reasoning scores of 96/100, 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-5.2 Chat and GPT-4.1 stack up head to head:
Best model by task
- coding: GPT-5.2 Chat wins with 94/100
- reasoning: GPT-5.2 Chat wins with 96/100
- data extraction: GPT-4.1 wins with 92/100
- 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 GPT-4.1 runs about $36.00 — GPT-4.1 saves roughly $9.50 (21%) 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, 9 months apart — so this is an upgrade question, not a choice between alternatives. GPT-5.2 Chat 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.
GPT-4.1 carries the larger context window at 1.0M tokens versus 256K for GPT-5.2 Chat. The gap is real but not decisive — it matters if your prompts routinely run long, and is irrelevant if they sit where most production prompts sit, well under 100K. Bear in mind that filling a large window is also what makes a request expensive.
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. GPT-5.2 Chat 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
Coding
Reasoning
Extraction
Creative
Vision
Speed Score
Context Window
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!"
- GPT-5.2 Chat
- $0.70
- GPT-4.1
- $0.80
Business email
One typical email (~200 words)
- GPT-5.2 Chat
- $47.25
- GPT-4.1
- $54.00
Code file
50-line Python script
- GPT-5.2 Chat
- $70.00
- 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-5.2 Chat 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_tokensEvery 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
GPT-5.2 Chat
$162.75/mo
$1,953.00/yr
GPT-4.1
$114.00/mo
$1,368.00/yr
Annual Savings
$585.00 saved per year
GPT-4.1 cheaper · $48.75/mo
Deep-Dive Audit — GPT-5.2 Chat & GPT-4.1
Surgically Auditing: Deep Logic
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%
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 ROUTINGGPT-5 Nano
IQ Score: 72/100
$18.00/yr
o3-mini
IQ Score: 97/100
$277.20/yr
DeepSeek R1
IQ Score: 97/100
$59.184/yr
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)| Model | Benchmark | Input (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).
// 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);
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