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Models

Model releases, API pricing, and capability shifts from OpenAI, Anthropic, Google, and the open-weight world. Model economics set the cost basis of every AI service an MSP builds or resells, and they change monthly. Staying model-agnostic is margin protection; this beat is where you watch the ground move.

Models · 2 stories this edition Ed. 029

Models

VentureBeat · Oct 7

Anthropic's Claude Haiku 5.5 cuts small-model prices by 90%, to $0.10 per million input tokens

Anthropic released Claude Haiku 5.5 at $0.10 per million input tokens and $0.50 per million output tokens for requests under 100,000 tokens, against $1 and $5 for Haiku 4.5, and $0.50 and $2.50 above that size. Anthropic estimates real workloads cost about 75% less after a tokenizer change, and says about 90% of Haiku 4.5 requests fall in the cheaper tier. It also halved Sonnet 5.5 cache reads to $0.10 per million tokens, and on its own benchmarks Haiku 5.5 scored 72.4% on OSWorld 2.1 against 83.9% for Sonnet 5.5.

▸ The MSP Angle

Is it worth switching our AI agents to a cheaper small model?

For high-volume steps such as ticket triage, summarizing documents, classifying email and routing requests, a small model at a tenth of last year's price changes the math on what you can automate for a fixed monthly fee. Rerun your own test set before you switch, because vendor benchmarks are not your workload, and keep the larger model for the steps that need judgment. Then decide how much of the saving you pass on to clients and how much becomes margin.

Read at VentureBeat ↗

Models

VKTR · Oct 6

Mistral previewed Large 4, a 1-trillion-parameter model with open weights due this month

Mistral opened an API preview of Mistral Large 4, a mixture-of-experts model with 1 trillion parameters, of which 49 billion are active at a time, priced at $1.36 per million input tokens and $4.18 per million output tokens. Mistral plans to publish the open weights by the end of October and says the model supports more than 160 languages. On its own tests it scored 82% on reproducing and patching a real open-source vulnerability and 59.9% on a business workflow benchmark, results that have not yet been independently verified.

▸ The MSP Angle

Should we consider open-weight models for clients with data residency needs?

Yes for clients who need data kept in a specific region or on their own infrastructure, since open weights let you run a large model where the data already lives. Budget for real hosting costs and patching, and wait for independent benchmarks before promising quality. For most small clients, a hosted model with clear data terms is still the simpler choice.

Read at VKTR ↗

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