--- title: "LLM API Pricing Compared, Cheapest First" description: "Every token-priced model ordered by input price, from $0.14 to $10 per million, with the cached-input rates that decide what an agent really costs." source: "https://www.kensink.com/models/pricing/" canonical: "https://www.kensink.com/models/pricing/" --- ★ 12 MODELS Model collection MODEL COLLECTION · PRICE PER MILLION # What the models cost, cheapest first. Every token-priced model in our index, ordered by input price. The spread is roughly seventy to one between the cheapest usable tier and the frontier, which is wide enough that routing by task difficulty is not an optimisation, it is the architecture. The cached-input column is the one that decides agent economics, and it is the one most comparisons leave out. [Talk to us about model choice →](https://www.kensink.com/contact) [Browse by family](https://www.kensink.com/models/) \[HOW WE BUILT THIS\] List prices per million tokens from each vendor's own price list, standard tier, checked on 8 September 2026. Batch and flex discounts, long-context surcharges and regional variation are noted below rather than in the table, because folding them into a single number would hide the thing that actually moves your bill. Self-hosted models are excluded here and covered in the open-weight collection, since their cost is hardware and engineering rather than tokens. | Model | Vendor | $ / 1M in · out | Context | Weights | Released | | --- | --- | --- | --- | --- | --- | | 01 [Qwen3.8-Flash](https://www.kensink.com/models/qwen/) The cheapest usable tier we would put in production, flat across a 1M context with no long-context surcharge. Roughly seventy times cheaper than the frontier on input. | Alibaba | $0.14 · $0.42 | 1M | Closed | 26 Aug 2026 | | 02 [GPT-5.6 Luna](https://www.kensink.com/models/openai-gpt/) OpenAI's cheap tier. Classification, routing and extraction, which is where most of an agent's token volume actually goes once you measure it. | OpenAI | $0.20 · $1.20 $0.02 cached | 1.05M | Closed | 26 Jun 2026 | | 03 Gemini 3.8 Flash Google's newest Flash, introductory pricing at $0.75 / $3.75 through 31 December 2026 and double that from January. | Google | $0.75 · $3.75 | — | Closed | Sep 2026 | | 04 [GLM-5.3](https://www.kensink.com/models/glm/glm-5-3/) zai-org/GLM-5.3 Level with GPT-5.6 Sol on Terminal-Bench 2.1 at a seventh of the input price, and state of the art on CyberGym vulnerability discovery. | Z.ai | $1.40 · $4.40 $0.26 cached | 1M | Open | 25 Aug 2026 | | 05 [GPT-5.6 Terra](https://www.kensink.com/models/openai-gpt/) The balanced OpenAI tier, and where most of the token volume belongs in a working system. Same context and tool surface as Sol. | OpenAI | $2 · $12 $0.20 cached | 1.05M | Closed | 26 Jun 2026 | | 06 [Qwen3.8-Max](https://www.kensink.com/models/qwen/) Alibaba's hosted flagship with no public weights, at a fifth of Claude Opus 5 on input. The Beijing endpoint runs 60 to 70% cheaper than Singapore. | Alibaba | $2 · $6 $0.25 cached | 1M | Closed | 3 Aug 2026 | | 07 [GPT-5.6 Sol](https://www.kensink.com/models/openai-gpt/gpt-5-6/) The prior OpenAI flagship and still the right default for most production work at two and a half times less than Astra. | OpenAI | $4 · $20 $0.40 cached | 1.05M | Closed | 26 Jun 2026 | | 08 [Claude Opus 5](https://www.kensink.com/models/claude/opus-5/) Anthropic's recommended starting tier and the coding arena leader. 63.1 on the Artificial Analysis index at half Fable pricing. | Anthropic | $5 · $25 | 1M | Closed | 24 Jul 2026 | | 09 [GPT-Image-2](https://www.kensink.com/models/vision/gpt-image-2/) Token-priced rather than per-image, so you cannot quote a cost per picture until you measure your own prompts. | OpenAI | $5 · $30 $1.25 cached | — | Closed | Apr 2026 | | 10 [GPT-6 Astra](https://www.kensink.com/models/openai-gpt/gpt-6-astra/) First model rated Critical for cyber under the Preparedness Framework. Records on computer use and terminals, fourth on a third-party intelligence index. | OpenAI | $10 · $50 $1 cached | 1.05M | Closed | 3 Sep 2026 | | 11 [Claude Fable 5.1](https://www.kensink.com/models/claude/fable-5-1/) Tops the Artificial Analysis Intelligence Index at roughly 66. Cache reads at $0.25, a quarter of Fable 5 and of every other Claude model. | Anthropic | $10 · $50 $0.25 cached | 1M | Closed | 1 Sep 2026 | | 12 [Claude Mythos 5.1](https://www.kensink.com/models/claude/fable-5-1/) Fable 5.1's specs and price, restricted to Project Glasswing participants. Scores 60.9 on Terminal-Bench 4.0 against Fable's 55.8. | Anthropic | $10 · $50 $0.25 cached | 1M | Closed | 1 Sep 2026 | Every row carries the date we last verified it. Prices are list rates at the standard tier and exclude batch discounts, long-context surcharges and regional variation. Hugging Face download and like counts are pulled from the API rather than retyped, and they measure adoption rather than quality. \[WHAT THE TABLE DOES NOT SAY\] ## Reading it properly. 01 ### Cached input is where a long-running agent's bill is decided. Claude Fable 5.1 reads cached input at $0.25 per million against GPT-6 Astra's $1, on identical $10 headline pricing. GLM-5.3 charges $0.26 against a $1.40 base. An agent re-reads its stable prefix on every turn, so on a long session the cache rate matters more than the base rate does. Measure your own cache-hit ratio before modelling any saving. 02 ### The spread is seventy to one, which changes what is worth building. Qwen3.8-Flash at $0.14 input against GPT-6 Astra at $10. At the cheap end, steps become worth doing that were not worth doing before: classifying every inbound message, extracting structure from every document, reviewing every call. Treating one frontier model as the default for all of it is the most common and most expensive architectural mistake we see. 03 ### The headline rate is not the rate you pay. Batch and flex run at 50% on most vendors. Fast modes run at 2x. GPT-6 Astra reprices the entire request above 272,000 input tokens. Alibaba's Beijing endpoint is 60 to 70% cheaper than Singapore, which is a data-residency decision before it is a pricing one. Model your actual traffic shape rather than multiplying volume by a list price. 04 ### A flat rate beats a discount when spend is unpredictable. Z.ai's Coding Plan runs $18 to $168 a month. Almost nobody else offers a flat rate, and unpredictable agent spend causes more internal friction than high agent spend does, because one bad loop can cost more than a fortnight of normal use. For a team running coding agents daily this is often worth more than a lower per-token price. \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. \[COMMON QUESTIONS\] ## Questions we are getting asked. What is the cheapest model that is actually usable? Qwen3.8-Flash at $0.14 input and $0.42 output, flat across a 1M context with no long-context surcharge. GPT-5.6 Luna is close behind at $0.20 / $1.20. Both are genuinely capable for classification, routing and extraction, which is where most of an agent's token volume actually goes once you measure it. How much can routing actually save? It depends entirely on your traffic mix, so any single figure would be marketing. The useful exercise is to instrument which steps need judgement and which do not, then price the split. On the systems we have built, the majority of calls turn out not to need the top tier, and the saving from moving those is usually larger than any discount available from switching vendors. Why do you exclude self-hosted models from this table? Because their cost is GPU hours, engineering time and on-call rather than tokens, and putting a zero in a price column would be actively misleading. They are covered in the open-weight collection, where the relevant columns are licence, parameters and memory footprint. Do these prices include reasoning tokens? Reasoning tokens bill as output on every vendor here, and on models with high default effort that is a large share of the bill. GPT-6 Astra has five effort levels up to max, and every headline benchmark for it was run at maximum. Price at the effort level you will actually use in production, not the one in the launch post. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fpricing%2F&text=Pricing)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fpricing%2F) [View .md](https://www.kensink.com/models/pricing.md) \[OTHER COLLECTIONS\] ## Same models, different question. [ COLLECTION The top 50 View ](https://www.kensink.com/models/top-50/)[ COLLECTION Open weights View ](https://www.kensink.com/models/open-weight-2026/)[ COLLECTION Coding View ](https://www.kensink.com/models/coding/)[ COLLECTION Long context View ](https://www.kensink.com/models/long-context/) DIRECT INTEGRATION · NO FRAMEWORK ## Picking a model is a routing decision. We build behind a vendor-neutral abstraction and route by task difficulty at runtime, with an eval suite that decides rather than a launch post. Eval suite at handoff, full source ownership. [Start a conversation →](https://www.kensink.com/contact) [Browse model families](https://www.kensink.com/models/)