★ Standard Model
Universal Credits powered by GPT-5 mini
100
Credits
=
117.65 M
Tokens
Based on standard usage patterns with a 9:1 input-to-output ratio. Credits work across all models — token allocations vary by model capability.
OpenAIAnthropicGooglePerplexity⬆ More tokens than GPT-5 mini
O
OpenAI
GPT-5.6, GPT-5.5, GPT-5, GPT-4.1, GPT-4o, o-series, GPT Image, and DALL-E
| Model | Input Tokens / 100 CR | Output Tokens / 100 CR |
|---|---|---|
| GPT-5 miniStandard General chat, analysis, and light coding | 200M | 25M |
| DALL-E 3 Creative illustrations, concepts, and marketing-style imagery | 10M | 1.56M |
| GPT Image 1.5 Strong image quality, variations, and multimodal workflows at scale | 10M | 1.56M |
| GPT Image 2 Latest image generation with rich detail, edits, and complex scenes | 10M | 1.67M |
| GPT-4.1 Strong coding and long-context instruction following | 25M | 6.25M |
| GPT-4.1 mini Cost-effective coding, extraction, and everyday assistants | 125M | 31.25M |
| GPT-4.1 nano High-volume classification, routing, and low-latency tasks | 500M | 125M |
| GPT-4o Vision and general multimodal assistants | 20M | 5M |
| GPT-4o mini Budget multimodal chat, vision, and simple extraction | 333.33M | 83.33M |
| GPT-5 Reasoning, coding, and general-purpose assistants | 40M | 5M |
| GPT-5 nano Summaries, tagging, and high-volume triage | 1B | 125M |
| GPT-5 Pro Deep analysis and planning with extended thinking | 3.33M | 416.67K |
| GPT-5.1 Everyday coding and agent-style tasks | 40M | 5M |
| GPT-5.1 Codex Economical coding, implementation, and code review | 40M | 5M |
| GPT-5.2 Coding, tool use, and multimodal workflows | 28.57M | 3.57M |
| GPT-5.2 Codex IDE-style edits, debugging, and repo-aware changes | 28.57M | 3.57M |
| GPT-5.2 Pro Very demanding reasoning, long-form analysis, complex decisions | 2.38M | 297.62K |
| GPT-5.3 Codex Agentic coding, refactors, and multi-file software work | 28.57M | 3.57M |
| GPT-5.4 Coding, agents, math, and broad analysis | 20M | 3.33M |
| GPT-5.4 Mini Quick iterations, lighter coding, and tool-heavy workflows | 66.67M | 11.11M |
| GPT-5.4 Nano Snippets, small fixes, classification, and routing | 250M | 40M |
| GPT-5.4 Pro Hardest problems, maximum precision, long-running work | 1.67M | 277.78K |
| GPT-5.5 Flagship for coding, agents, computer-use, and professional work | 10M | 1.67M |
| GPT-5.5 Pro Highest-precision reasoning, audits, and high-stakes professional work | 1.67M | 277.78K |
| GPT-5.6 Flagship Sol tier for hardest reasoning, agents, and professional work | 10M | 1.67M |
| GPT-5.6 Luna High-volume extraction, classification, drafts, and cost-sensitive tasks | 50M | 8.33M |
| GPT-5.6 Terra Balanced production coding, analysis, and everyday professional work | 20M | 3.33M |
| o3 STEM, logic, and careful step-by-step analysis | 25M | 6.25M |
| o3 Deep Research Long reports, due diligence, and literature-style synthesis | 5M | 1.25M |
| o4-mini Cost-efficient math, logic, and structured reasoning | 45.45M | 11.36M |
| o4-mini Deep Research Faster, more affordable deep research model | 25M | 6.25M |
A
Anthropic
Claude Fable, Opus, Sonnet, and Haiku model families
| Model | Input Tokens / 100 CR | Output Tokens / 100 CR |
|---|---|---|
| Claude Fable 5 Frontier Mythos-class reasoning with stronger safeguards for high-risk domains | 5M | 1M |
| Claude Haiku 3 Maximum economy and minimum latency at high scale | 200M | 40M |
| Claude Haiku 4.5 High-volume assistants and chat with strong answer quality | 50M | 10M |
| Claude Opus 4 Broad flagship-quality coding, analysis, and writing | 3.33M | 666.67K |
| Claude Opus 4.1 Premium coding, instruction following, and high-stakes work | 3.33M | 666.67K |
| Claude Opus 4.5 Elite reasoning, research depth, and strategic analysis | 10M | 2M |
| Claude Opus 4.6 Hardest agents, complex coding, and very long documents | 10M | 2M |
| Claude Opus 4.7 Stronger coding, vision, and complex multi-step tasks with improved consistency | 10M | 2M |
| Claude Opus 4.8 Most capable Opus for complex reasoning, long-horizon agentic coding, and high-autonomy work | 10M | 2M |
| Claude Sonnet 4 Implementation, documentation, and general software tasks | 16.67M | 3.33M |
| Claude Sonnet 4.5 Product engineering, coding, and long-context sessions (up to 1M with opt-in context) | 16.67M | 3.33M |
| Claude Sonnet 4.6 Coding, agent planning, and drafting with strong long-context reasoning | 16.67M | 3.33M |
| Claude Sonnet 5 Default Sonnet for agents, coding, and near-Opus quality at lower cost | 16.67M | 3.33M |
G
Google Gemini
Gemini Pro, Flash, and Flash-Lite models
| Model | Input Tokens / 100 CR | Output Tokens / 100 CR |
|---|---|---|
| Gemini 2.0 Flash Balanced multimodal assistant | 500M | 125M |
| Gemini 2.0 Flash-Lite Simple input–output and minimum spend per request | 666.67M | 166.67M |
| Gemini 2.5 Flash Chat, summarization, and extraction at large scale | 166.67M | 20M |
| Gemini 2.5 Flash-Lite Routing, translation, and extremely high request volume | 500M | 125M |
| Gemini 2.5 Pro Complex code, deep analysis, and very long context | 40M | 5M |
| Gemini 3 Flash Fast multimodal work, thinking-style tasks, large context | 100M | 16.67M |
| Gemini 3 Pro Complex analysis, coding, and multimodal assistants (preview) | 25M | 4.17M |
| Gemini 3.1 Flash-Lite High-throughput tasks where cost and speed both matter | 200M | 33.33M |
| Gemini 3.1 Pro Agents, coding, and multimodal reasoning (preview) | 25M | 4.17M |
| Gemini 3.5 Flash Fast reasoning, multimodal agents, and large-scale production workloads | 33.33M | 5.56M |
P
Perplexity
Sonar search-grounded models
| Model | Input Tokens / 100 CR | Output Tokens / 100 CR |
|---|---|---|
| Sonar Web-grounded answers | 50M | 50M |
| Sonar Deep Research Long reports, due diligence, and literature-style synthesis | 25M | 6.25M |
| Sonar Pro Multi-step research questions, broad sources, nuanced synthesis | 16.67M | 3.33M |
| Sonar Reasoning Structured analysis grounded in live search results | 50M | 10M |
| Sonar Reasoning Pro Deep multi-step reasoning over retrieved web evidence | 25M | 6.25M |
Understanding This Rate Card
- Higher-capability models consume more credits per token
- Efficient models stretch your credits further
- All values based on 100 Credits
- GPT-5 mini is the baseline standard model
- Green values indicate more tokens than GPT-5 mini
- Rates are subject to change by providers
- Actual usage varies based on conversation patterns
© 2026 Expedient. All rights reserved. | AI CTRL Platform Documentation
Page last updated: 7/23/2026, 6:00:31 AM ET
Page last updated: 7/23/2026, 6:00:31 AM ET