01
Model pricing
Input, output, blended, and workload-sensitive token costs across leading AI models and providers.
AI ECONOMICS INTELLIGENCE
The intelligence layer for AI model economics.
Tokenando tracks AI model pricing, token costs, provider shifts, and compute-market signals so builders, executives, investors, and procurement teams can understand the real economics of AI.
Live model pricing, workload cost tools, provider intelligence, and weekly AI economics briefings.
The problem
AI pricing is now a moving market, not a static table.
Model costs change. Context windows expand. Output tokens alter margins. Aggregators introduce new routes. Benchmarks shift. Compute availability affects pricing power. Yet most teams still make AI infrastructure decisions with stale spreadsheets, scattered provider pages, and incomplete cost assumptions.
- Provider pages change faster than internal models.
- Input and output pricing behave differently.
- Context windows reshape workload economics.
- Benchmarks without cost are incomplete.
- Procurement needs evidence, not guesses.
Coverage
What Tokenando tracks
Tokenando turns scattered AI pricing and model data into a structured intelligence layer for decision-making.
02
Provider strategy
Track how OpenAI, Anthropic, Google, Mistral, Meta, DeepSeek, xAI, Cohere, and others position their models.
03
Context windows
Understand which models can handle long-context, retrieval, agentic, and enterprise workloads.
04
Model metadata
Use model-level pages with pricing, benchmarks, use cases, provider data, and related alternatives.
05
Compute signals
Follow the infrastructure layer behind AI economics, from GPU supply to inference competition.
06
Market intelligence
Connect pricing changes, model launches, infrastructure trends, and strategic shifts into one view.
The structuring layer
From fragmented providers to structured intelligence
Tokenando transforms scattered model and pricing information into a structured intelligence layer that businesses can actually use.
AAnthropicOOpenAIGGooglexxAIMMetaDDeepSeekFragmented providers & models
Provider · ModelInputOutputContextBlended
The platform
One platform for the economics of AI.
Tokenando combines live pricing data, model intelligence, cost tools, market context, and strategic briefings.
Pricing Index
Compare AI models by input price, output price, context window, provider, and blended cost. Filter across direct providers and inference channels.
Open pricing index →Model Pages
Go deeper on individual models with dedicated profiles covering pricing, metadata, use cases, benchmarks, and related alternatives.
Browse models →Calculator
Estimate workload cost by token volume, input/output mix, model choice, and usage pattern.
Try calculator →Compare
Compare model economics side by side so decisions are based on cost, capability, and context, not hype.
Compare models →Market Context
Understand the broader AI pricing market with provider comparisons, category breakdowns, and market-level statistics.
View market context →The Compute
Read strategic briefings on AI pricing, compute markets, model launches, benchmarks, infrastructure, and provider strategy.
Read The Compute →How it works
From prompt to cost, every token matters.
Tokenando helps teams understand how model choice, input volume, output volume, context, and provider pricing translate into real infrastructure cost.
- User prompt enters the system.
- Prompt is tokenised into input tokens.
- The model is selected.
- Context window is shown.
- Output tokens are generated.
- Provider pricing is applied.
- Final cost is calculated.
- Workload-level monthly cost appears.
Input tokens × input price + output tokens × output price = workload cost
Example: a support-agent workload with 10M input tokens and 4M output tokens behaves very differently from a summarisation workload with 90M input tokens and 5M output tokens.
Estimated monthly cost
$0.70
Ling-2.6-flash · inclusionai
40M input + 10M output tokens / month at public pricing. Actual cost varies with caching, batch APIs, routing, and enterprise terms.
Who it's for
Built for the teams making AI economic decisions.
AI builders & product teams
Choose models by workload, not hype.
Evaluate models based on the economics of your actual application, from RAG and agents to batch processing, summarisation, support automation, and long-context workflows.
- Select cheaper models for high-volume features
- Compare direct provider versus inference channel costs
- Understand cost impact before launch
- Track model alternatives
Executives & operators
Understand the cost structure behind AI strategy.
See how model selection, token volume, provider mix, and compute trends influence margins, product pricing, and AI investment decisions.
- Build AI roadmaps with cost visibility
- Monitor vendor exposure
- Understand model economics at board level
- Identify strategic shifts in the market
Investors & analysts
Track the economics behind the AI infrastructure race.
Follow pricing moves, model releases, compute constraints, and provider behaviour as signals of strategy, competition, and market structure.
- Monitor model commoditisation
- Understand inference competition
- Analyse provider positioning
- Track the cost of intelligence over time
Finance & procurement
Benchmark vendors before costs surprise you.
Use Tokenando to benchmark model pricing, compare provider routes, prepare vendor discussions, and understand how usage patterns affect spend.
- Validate provider pricing
- Model spend scenarios
- Support procurement negotiations
- Reduce spreadsheet dependency
Live data
The AI model market, at a glance.
Tokenando structures pricing and model data into a live view of the market.
Models tracked
561
Providers tracked
66
Direct providers
39
Inference providers
51
Avg blended / 1M
$3.12
Median blended / 1M
$0.65
Cheapest input
$0.00
Whisper Large v3 · Replicate
Cheapest output
$0.02
Gemma 2 2B · Google
Largest context
2M
Gemini 1.5 Pro · Google
Last updated
25 Jun 2026
Figures depend on available public pricing data and may change as providers update models, routes, and commercial terms.
Why now
AI pricing is becoming financial infrastructure.
Token costs are no longer a technical detail. They affect margins, procurement, product architecture, vendor strategy, and investment decisions. As AI moves from experiments to production systems, the teams that understand model economics will make better decisions.
AI usage is moving from pilots to production
When AI features scale, token cost becomes a recurring operating expense.
Output tokens can reshape margins
Two models with similar capabilities can produce very different economics depending on output volume.
Model markets are fragmenting
Frontier labs, open-source models, aggregators, and specialised inference providers are creating a more complex buying landscape.
Benchmarks need economic context
Capability alone is not enough. Teams need to understand price, latency, context, throughput, and workload fit.
Methodology
Built for trust, not hype.
Tokenando's value depends on clarity, consistency, and transparent methodology.
Blended cost = 70% input price + 30% output price
Source transparency
Pricing is collected from public provider pages, official API pricing pages, and supported inference channels where available.
Normalised pricing
Prices are normalised to comparable token-based units where possible, including input and output pricing.
Blended cost logic
Tokenando uses a default blended cost metric to make broad comparison easier, while clearly separating input and output economics.
Direct versus aggregator
Direct provider pricing and inference-channel pricing are clearly separated where routes differ.
Update visibility
Every pricing view shows when it was last updated or refreshed.
Caveats
Actual costs may vary by caching, batch APIs, enterprise agreements, routing, region, latency, throughput, and usage pattern.
The Compute
The Compute: weekly intelligence on the economics of AI.
Pricing moves, GPU markets, model launches, infrastructure strategy, benchmarks, and provider shifts, explained for people making decisions.
Briefing 01
Why output tokens are the hidden margin killer
Briefing 02
The race to cheaper intelligence
Briefing 03
What model pricing reveals about provider strategy
Briefing 04
How context windows changed AI cost modelling
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Track the economics of AI before they move against you.
Explore live model pricing, compare provider economics, and follow the market forces shaping the cost of intelligence.