Compare AI model costs for your actual workload
Apply the same request volume, token mix, cache usage, and retry assumption to every reviewed model. Compare projected spend, then weigh quality and production constraints before choosing.
Define one comparable workload
Use measured production values when possible. Presets are editable examples, not typical-use claims.
Fresh and cached input are separate fields. Retry rate adds repeated calls uniformly; it does not estimate model-specific quality or failure rates.
Projected token cost by model
Pricing data last checked: Aug 22, 2026
Lowest token-cost projection
DeepSeek V4 Flash
$15.40 per month
Effective monthly calls
10,000
Base requests plus stated retries
Cost range
16.2×
Highest ÷ lowest in the current filter
| Cost rank | Model | Projected monthly cost | Cost per request | Context window | Best use cases | Source |
|---|---|---|---|---|---|---|
| 1 | DeepSeek V4 Flash DeepSeek | $15.40 | $0.001540 | 1M | Low-cost general and reasoning workloads | Official |
| 2 | Gemini 3.5 Flash-Lite Google | $18.50 | $0.001850 | See provider model limits | High-volume translation and simple processing | Official |
| 3 | Gemini 3.7 Flash Google | $33.75 | $0.003375 | See provider model limits | Agentic workflows and multimodal reasoning | Official |
| 4 | Claude Haiku 4.5 Anthropic | $45.00 | $0.004500 | See provider model limits | Fast, lower-cost Claude workloads | Official |
| 5 | DeepSeek V4 Pro DeepSeek | $46.20 | $0.004620 | 1M | Higher-capability DeepSeek workloads | Official |
| 6 | GPT-5.6 Luna OpenAI | $50.00 | $0.005000 | Short-context tier | Cost-sensitive, high-volume workloads | Official |
| 7 | Gemini 3.5 Flash Google | $75.00 | $0.007500 | See provider model limits | Fast grounded and search-oriented workloads | Official |
| 8 | Claude Sonnet 5 Anthropic | $90.00 | $0.009000 | See provider model limits | General production, coding and agents | Official |
| 9 | GPT-5.6 Terra OpenAI | $125.00 | $0.01 | Short-context tier | Balanced intelligence and cost | Official |
| 10 | Claude Opus 5 Anthropic | $225.00 | $0.02 | See provider model limits | High-capability analysis and agentic work | Official |
| 11 | GPT-5.6 Sol OpenAI | $250.00 | $0.03 | Short-context tier | Complex professional reasoning and coding | Official |
Formula: monthly cost = requests × (1 + retry rate) × ((fresh input × input rate + cached input × cached rate + output × output rate) ÷ 1,000,000).
Disclaimer: This compares token charges under shared assumptions, not model quality, task success, latency, rate limits, taxes, regional tiers, tools, storage, embeddings, or other fees. The lowest projection is not a recommendation. Rates are dated, manually reviewed references—not real-time data—so verify official terms before purchasing.
The cheapest model is not always the best choice
A low per-token price looks attractive on a spreadsheet, but production AI costs are shaped by more than the rate card. Weigh these three tradeoffs before locking in a model for your application.
Accuracy vs. cost
Cheaper models often need more retries, longer prompts, or extra verification steps to reach the same output quality. A model with a higher token price but fewer failed generations can end up cheaper per successful task — and it protects the user experience.
Latency vs. cost
Smaller, cheaper models are usually faster, which matters for real-time chat or agent loops. Larger flagship models can be slower even though they reason better — so the "best" model depends on whether your product needs instant responses or can tolerate a few extra seconds for higher-quality output.
Production requirements
Rate limits, context window size, function-calling support, uptime, and data-handling policies all differ by provider and tier. A model that's cheap in isolated testing can become the expensive choice once you factor in outages, throttling, or the engineering time needed to work around its limitations at scale.
Rule of thumb: use a fast, cheap model for high-volume, low-risk tasks (classification, extraction, simple chat), and reserve a higher-accuracy model for the steps where a mistake is costly — then measure real cost per successful outcome, not just cost per token.
Inspect one model in more detail
Open the calculator for a single-model breakdown, currency conversion, and volume projections.