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Token Pricing

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Claude Fable 5.2 tops Artificial Analysis Intelligence Index with score of 65

Claude Fable 5.2 (Max, Default Fallback) achieved a score of 65 on the Artificial Analysis Intelligence Index, well above the comparable median of 40. The model supports text and image input with text output and a 1M token context window, but testing showed it used 275M tokens versus a 72.8M median, took 45 minutes per task against a 12.9 minute median, and cost $26.54 per task to evaluate. Input pricing is $8.00 per 1M tokens and output pricing is $40.00 per 1M tokens, both well above typical rates.

Darkbloom warns Mac compute-sharing users of falling token prices cutting earnings

Darkbloom, a network that lets Mac owners rent out idle compute for AI inference, told members that falling AI token prices will shrink their earnings. The company cited data showing Anthropic's price per million tokens fell 77% between May and October 2026, driven by cheaper, more efficient data center hardware. Darkbloom estimates top earners could see monthly income drop from around $160 to roughly $37 as a result.

Hacker News debate questions why OpenAI and Anthropic sell AI tokens instead of building products

A discussion thread on Hacker News raises the question of why leading AI labs like OpenAI and Anthropic primarily sell model access as tokens rather than using their own frontier models to build and launch consumer or enterprise products directly. The poster argues that as token prices fall due to commodification, more profit potential may lie in building applications on top of models rather than selling raw model access.

Google's Gemini 4 Argon (High) scores 53 on Artificial Analysis Intelligence Index

Artificial Analysis benchmarked Gemini 4 Argon (High), a model that accepts text and image input and outputs text with a 1M token context window. It scored 53 on the firm's Intelligence Index, well above the median of 26, while using 110M tokens during evaluation versus a median of 82M. Pricing is set at $2.00 per 1M input tokens and $10.00 per 1M output tokens, both matching the median for comparable models.

Claude Opus 5.5 scores 58 on Artificial Analysis Intelligence Index, priced above median

Anthropic's Claude Opus 5.5, running in Adaptive Reasoning Max Effort mode, scored 58 on the Artificial Analysis Intelligence Index against a median of 25 among comparable models. The model handles text and image input with text output and a 1M token context window, but is priced at $4.00 per 1M input tokens and $20.00 per 1M output tokens, both above the reported medians of $2.00 and $10.00. Evaluating it on the Intelligence Index generated 260M tokens and cost $8,708.20 in total.

MiMo-V2.6-Pro debuts as fast, high-scoring open-weight multimodal model

MiMo-V2.6-Pro is a new open-weight AI model supporting text, image, speech, and video inputs with a 1M-token context window, scoring 46 on the Artificial Analysis Intelligence Index versus a median of 18 among comparable models. It runs at 125 tokens per second, priced at $0.43 per million input tokens and $0.87 per million output tokens, with a total evaluation cost of $206.66.

Mid-tier AI models now match flagship performance at fraction of the price, data shows

New industry data shows token usage for AI models has surged more than 25-fold over the past year, doubling in just the last month, even as per-token costs for high-intelligence models continue to fall. Mid-tier models from providers like Google and Meta are now delivering roughly 90% of the capability of pricier flagship systems such as Anthropic's Claude Opus at around one-sixth the cost, intensifying competition on what analysts call the 'Pareto Frontier' of price versus intelligence.

Anthropic ships Claude Fable 5.1 and restricted Mythos 5.1 variant, slashes cached token costs 75%

Anthropic launched Claude Fable 5.1 across its API, cloud platforms and desktop app, alongside Mythos 5.1, a less-restricted version limited to vetted cybersecurity and life-sciences organizations. The company says Fable 5.1 handles multistep coding and scientific workflows more efficiently, using fewer tokens, and posted large gains on internal benchmarks like Terminal-Bench 4.0 and Terminal-Bench-Science 0.1 versus its predecessor.