GPT-5.6 Sol
gpt-5-6-sol
70% in · 30% out mix
Higher = better value
Speed
70/100
Context
1.1M
Tier
power
GPT-5.6 Terra
gpt-5-6-terra
70% in · 30% out mix
Higher = better value
Speed
86/100
Context
1.1M
Tier
smart
IN-DEPTH ANALYSIS
GPT-5.6 Sol vs GPT-5.6 Terra: Detailed Comparison
GPT-5.6 Sol is OpenAI's flagship-tier language model with a 1.1M-token context window, excelling at reasoning. GPT-5.6 Terra from OpenAI is a mid-range-tier model supporting 1.1M tokens in context, with standout performance in reasoning.
GPT-5.6 Sol costs 2.5x what GPT-5.6 Terra does per blended million tokens. That is a steep premium, and it buys a 9-point lead on combined coding and reasoning. Whether that trade is worth it depends entirely on how much of your traffic actually needs the harder model — for most workloads the honest answer is a small fraction of it, which is an argument for routing rather than for picking one. GPT-5.6 Sol is priced at $5.00/M input tokens and $30.00/M output tokens. GPT-5.6 Terra costs $2.00/M input and $12.00/M output.
In independent benchmark evaluations, GPT-5.6 Sol leads with coding scores of 97/100 and reasoning scores of 99/100, compared to GPT-5.6 Terra's 93/100 in coding and 94/100 in reasoning.
Capability breakdown
Across the five core benchmark categories, here is how GPT-5.6 Sol and GPT-5.6 Terra stack up head to head:
Best model by task
- coding: GPT-5.6 Sol wins with 97/100
- reasoning: GPT-5.6 Sol wins with 99/100
- data extraction: GPT-5.6 Sol wins with 95/100
- creative tasks: GPT-5.6 Sol wins with 96/100
- 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 GPT-5.6 Terra runs about $44.00 — GPT-5.6 Terra saves roughly $66.00 (60%) 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.
GPT-5.6 Sol and GPT-5.6 Terra were released within 0 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: 1.1M against 1.1M. 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.
Latency separates them: GPT-5.6 Terra scores 86/100 against 70/100 for GPT-5.6 Sol, a 16-point gap. That matters for anything a person waits on — chat, autocomplete, interactive tools. For batch and background work it does not, and trading latency for capability there is usually the right call.
GPT-5.6 Sol is 2.5x the price of GPT-5.6 Terra. Reserve it for the requests that actually need it and route the rest to GPT-5.6 Terra — that hybrid beats either model used alone on cost per useful answer.
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-5.6 Sol
- $2.00
- GPT-5.6 Terra
- $0.80
Business email
One typical email (~200 words)
- GPT-5.6 Sol
- $135.00
- GPT-5.6 Terra
- $54.00
Code file
50-line Python script
- GPT-5.6 Sol
- $200.00
- GPT-5.6 Terra
- $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 Sol and GPT-5.6 Terra, 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-5.6 Sol
$375.00/mo
$4,500.00/yr
GPT-5.6 Terra
$150.00/mo
$1,800.00/yr
Annual Savings
$2,700.00 saved per year
GPT-5.6 Terra cheaper · $225.00/mo
Deep-Dive Audit — GPT-5.6 Sol & GPT-5.6 Terra
Surgically Auditing: Deep Logic
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%
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 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 $4,685.616/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 |
|---|---|---|---|---|---|
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 |
o3-mini | 97/100 | $1.10 | $4.40 | $66.00 | 50/100 |
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 |
GPT-5.2 Chat | 96/100 | $1.75 | $14.00 | $189.00 | 17/100 |
Claude 3.7 Sonnet | 95/100 | $3.00 | $15.00 | $216.00 | 15/100 |
GPT-5.6 Terra | 94/100 | $2.00 | $12.00 | $168.00 | 19/100 |
Claude 3.5 Sonnet | 93/100 | $3.00 | $15.00 | $216.00 | 15/100 |
GPT-4.1 | 93/100 | $2.00 | $8.00 | $120.00 | 26/100 |
Claude Sonnet 5 | 92/100 | $3.00 | $15.00 | $216.00 | 14/100 |
Claude 3 Opus | 90/100 | $15.00 | $75.00 | $1,080.00 | 3/100 |
Grok 4.5 | 90/100 | $2.00 | $6.00 | $96.00 | 32/100 |
GPT-4o | 90/100 | $2.50 | $10.00 | $150.00 | 20/100 |
Gemini 3.1 Pro | 89/100 | $2.00 | $12.00 | $168.00 | 18/100 |
Gemini 2.0 Pro | 88/100 | $1.25 | $5.00 | $75.00 | 40/100 |
Llama 3.1 405B | 88/100 | $2.70 | $2.70 | $64.80 | 46/100 |
Gemini 1.5 Pro | 87/100 | $1.25 | $5.00 | $75.00 | 39/100 |
Mistral Large 2 | 86/100 | $2.00 | $6.00 | $96.00 | 30/100 |
* 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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