tokevenGPT · Flagship · 1,050K context window
GPT 5.6 Sol pricing for chatbot
$0.0280 per call at 2,000 input / 1,000 output tokens.
Per call
$0.0280
10K calls/mo
$280
Cache savings
90% off input
Batch savings
50% off all
Calculate your GPT 5.6 Sol costs
Adjust token counts, volume, and discount toggles to model your chatbot spend. Prices verified daily against provider pricing pages.
Configure your usage
Estimated cost
Quick start
Call GPT 5.6 Sol with the OpenAI Python SDK:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5.6-sol",
max_tokens=1000,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0280 per call at standard rates
# Input: $4.00/MTok
# Output: $20.00/MTokHow GPT 5.6 Sol compares on cost
Per-call cost for all models scoring 50+ on chatbot benchmarks. Hover for details.
Cheapest viable option: GPT 5.6 Luna at $0.001600/call (94% less, 85/100 quality).
What drives chatbot cost
Conversational cost compounds: every turn resends the history, so the tenth message in a thread costs several times the first. Cost grows with the square of conversation length unless you cache the prefix or truncate history. Prompt caching is not an optimization here, it is the difference between a viable and an unviable unit economic.
For GPT 5.6 Sol specifically, that means output tokens cost 5.0x more than input, at $20.00 vs $4.00 per MTok. At this use case’s 2,000/1,000 token split, output accounts for 71% of the $0.0280 per-call cost.
Complete GPT 5.6 Sol pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $4.00 | 2,000 |
| Output | $20.00 | 1,000 |
Prompt caching: 90% off input tokens
Caching drops input cost from $4.00 to $0.40/MTok. For repeated system prompts, that saves $0.007200 per call.
Batch API: 50% off all tokens
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
Monthly cost projections
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.0280 | $280 | $208 | $72 |
| 50,000 calls | $0.0280 | $1,400 | $1,040 | $360 |
| 100,000 calls | $0.0280 | $2,800 | $2,080 | $720 |
| 500,000 calls | $0.0280 | $14,000 | $10,400 | $3,600 |
| 1,000,000 calls | $0.0280 | $28,000 | $20,800 | $7,200 |
GPT 5.6 Sol quality benchmarks for chatbot
Use GPT 5.6 Sol for chatbot
Scores 91/100 (rank #1 of 20). Strong pick for production chatbot workloads.
Top-ranked: GPT 5.6 Terra (92/100) at $0.0160/call
Performance across all task types
GPT 5.6 Sol scores by task type. Chatbot highlighted.
| Task type | Confidence score | Rank out of 20 |
|---|---|---|
| Reasoning | 96/100 | #1 |
| Code Generation | 94/100 | #1 |
| Code Review | 92/100 | #2 |
| Creative Writing | 92/100 | #2 |
| Q&A(current page) | 91/100 | #1 |
| Summarization | 90/100 | #2 |
| Extraction | 88/100 | #3 |
| Classification | 86/100 | #3 |
Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
Alternative models for chatbot
12 models score 80+ on chatbot benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (94% less, 85/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs GPT |
|---|---|---|---|---|---|---|
| GPT 5.6 Luna | GPT | $0.20 | $1.20 | 85/100 | $0.001600 | 94% cheaper |
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 82/100 | $0.004000 | 86% cheaper |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 86/100 | $0.007000 | 75% cheaper |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 84/100 | $0.0120 | 57% cheaper |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 89/100 | $0.0140 | 50% cheaper |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 92/100 | $0.0160 | 43% cheaper |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 87/100 | $0.0160 | 43% cheaper |
| GPT 5.4 | GPT | $2.50 | $15.00 | 91/100 | $0.0200 | 29% cheaper |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 88/100 | $0.0210 | 25% cheaper |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 85/100 | $0.0350 | 25% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 90/100 | $0.0400 | 43% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 88/100 | $0.0700 | 150% more |
About GPT 5.6 Sol
Provider
GPT
Tier
Flagship
Context
1,050K tokens
Released
Jul 2026
Other models from GPT
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| GPT 5.5 | Flagship | $5.00 | $30.00 | 1,050K |
| GPT 5.4 | Mid-tier | $2.50 | $15.00 | 1,050K |
| GPT 5.6 Terra | Mid-tier | $2.00 | $12.00 | 1,050K |
| GPT 5.6 Luna | Fast / Budget | $0.20 | $1.20 | 1,050K |
You can compare GPT 5.6 Sol head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.
GPT 5.6 Sol pricing for other use cases
See how GPT 5.6 Sol costs change across different workload types.
Code Generation
$0.0880/call
Unit Test Generation
$0.1120/call
SQL Generation
$0.0260/call
API Generation
$0.1320/call
Code Refactoring
$0.0960/call
Code Review
$0.0600/call
PR Review
$0.0920/call
Security Review
$0.0740/call
Bug Detection
$0.0600/call
Document Summarization
$0.0600/call
PDF Summarization
$0.0900/call
Meeting Notes
$0.0720/call
Email Summarization
$0.0220/call
RAG Pipeline
$0.0540/call
Customer Support Bot
$0.0320/call
FAQ Bot
$0.0220/call
Data Extraction
$0.0360/call
JSON Extraction
$0.0280/call
Invoice Parsing
$0.0200/call
Resume Parsing
$0.0260/call
Chain of Thought
$0.1120/call
Legal Analysis
$0.1200/call
Math Reasoning
$0.0660/call
Financial Analysis
$0.1120/call
Content Moderation
$0.004000/call
Sentiment Analysis
$0.004000/call
Categorization
$0.006200/call
Intent Detection
$0.003200/call
Copywriting
$0.0420/call
Blog Writing
$0.1640/call
Marketing Copy
$0.0632/call
Product Descriptions
$0.0320/call
Related pages
Frequently asked questions
How much does GPT 5.6 Sol cost for chatbot per API call?
A typical chatbot call using GPT 5.6 Sol costs about $0.0280 per call. This estimate uses 2,000 input tokens and 1,000 output tokens at standard rates. Standard rates are $4.00 per million input tokens and $20.00 per million output tokens.
Is GPT 5.6 Sol the best model for chatbot?
GPT 5.6 Sol scores 91 out of 100 on chatbot benchmarks overall. It ranks #1 across all 20 models in our cross-provider registry. The highest-ranked model for this task is GPT 5.6 Terra from GPT. That model scores 92/100 on this task type. Confidence scores are a third-party benchmark snapshot from week 29 of 2026. This snapshot is 8 weeks old, so treat the scores as directional and validate against your own evals.
What is the cheapest model for chatbot with good quality?
The most affordable model for chatbot above 80/100 quality is GPT 5.6 Luna. It comes from GPT at $0.20 per million input tokens. Output costs $1.20 per million tokens with a quality score of 85/100. Switching from GPT 5.6 Sol to GPT 5.6 Luna saves about 94% per API call.
How can I reduce my GPT 5.6 Sol costs for chatbot?
Three strategies lower GPT 5.6 Sol costs for this task type effectively. Prompt caching gives a 90% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.0280 to about $0.001400 per call.
How does GPT 5.6 Sol pricing compare to other GPT models?
GPT has 4 other models alongside GPT 5.6 Sol today: GPT 5.5 at $5.00/$30.00 per million tokens (flagship), GPT 5.4 at $2.50/$15.00 per million tokens (mid-tier), GPT 5.6 Terra at $2.00/$12.00 per million tokens (mid-tier), GPT 5.6 Luna at $0.20/$1.20 per million tokens (fast / budget). The right choice depends on whether your chatbot workload prioritizes quality, cost, or speed.
What context window does GPT 5.6 Sol support?
GPT 5.6 Sol supports a context window of 1,050K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For chatbot, typical calls use 2,000 input and 1,000 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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