Tech News
← Home  ·  All topics

Token Pricing

4 GoKawiil briefs on this topic

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.