Anthropic's Claude Mythos shows significant performance advantages over OpenAI's GPT-5.4-xhigh, indicating a shift in AI capabilities that builders should monitor for potential opportunities in AI development and deployment.
Anthropic is obliterating OpenAI
Claude Mythos 77.8% on SWE-Bench Pro
20% higher than GPT-5.4-xhigh
A PhD student evaluates OpenAI's GPT-5.4 Pro, revealing its limitations in solving advanced research problems, which may inform pricing strategies and product development for AI tools.
A mathematics PhD student tested OpenAI’s GPT-5.4 Pro ($200/month)
to see if it actually justifies the price compared to the $20 plan.
Here’s what he found:
- Research problems: Could not solve the hardest ones, still struggles at true PhD-level questions
- Paper review: Very
The performance metrics of Claude Mythos and GPT-5.4-Pro highlight emerging trends in AI capabilities and pricing, providing builders with insights into competitive positioning and potential market opportunities.
Claude Mythos scores 161 on ECI
with a 95% CI from 158 to 166
GPT-5.4-Pro is at 158 which is a multi-agent system and costs $180/million
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AI performancemarket trendsClaude MythosGPT-5.4-ProAI pricing
Anthropic's mythos-preview shows significant performance benchmarks against Claude Opus, indicating a competitive edge in AI capabilities. Senior engineers should note these metrics as they reflect evolving standards in AI model performance.
you're laughing? anthropic's mythos-preview for which normies won't get access is scoring 77.8% vs 53.4% (claude opus 4.6) in swe-bench pro, 82 vs. 65.4 in terminal bench 2.0 and 93.8% vs 80.8% (opus) in swe-bench-verified and you're laughing?
Mythos has achieved a 70.8% score on AA-Omniscience, surpassing the previous SOTA of Gemini 3.1 Pro at 55%. This indicates a significant advancement in AI capabilities that could influence future developments in the field.
Mythos scores 70.8% on AA-Omniscience
the previous SOTA was Gemini 3.1 Pro with 55%
also insanely high scores on SimpleQA Verified
Muse Spark demonstrates notable token efficiency with 58M output tokens for its Intelligence Index, outperforming several competitors. This benchmark could inform decisions on model selection for resource-constrained applications.
Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5
Zuckerberg's investment in a young AI researcher has led to the launch of Muse Spark, which competes strongly against established models like Opus and GPT. This indicates a significant shift in AI capabilities and potential market direction.
Zuckerberg paid $14.3 billion for a 28-year-old who had never trained a frontier model. Nine months later, that bet just shipped.
The benchmark table tells you exactly what kind of lab Wang built. Muse Spark leads or ties Opus 4.6 and GPT 5.4 on multimodal perception, health
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