Benchmarking the Latest AI Models: The Rise of Extreme Models
The recent benchmarking of 40 new AI models brings to light significant shifts in the Price vs. Performance landscape. With attention focused on Kimi k2.5 and Claude Opus 4.6, the analysis reveals a divide into two extremes: 'God Mode' and 'Flash Mode', rendering mid-range models ineffective.
Key Details
- Kimi k2.5 Situation: Attempts to benchmark Kimi k2.5 were unsuccessful due to persistent 'No Content' errors, likely due to overload. However, Kimi-k2-Thinking performed adequately for complex reasoning tasks at ~15 TPS.
- Speed Dominance: For latency-sensitive applications, Liquid LFM 2.5 emerged as the speediest model clocking in at ~359 tokens/sec, followed by Ministral 3B at ~293 tokens/sec.
- Cost Efficiency: Ministral 3B stands out as the most cost-effective solution, at $0.10/1M input tokens. It is ~17x cheaper and ~40% faster than GPT-5.2 Codex, making it a strong value play against higher-priced options.
The recommendation is to avoid mid-range models that cost between $0.50 - $1.00, as they do not offer competitive performance. Depending on your needs, choose higher-priced models like Opus/GPT-5 for intelligence or opt for cost-effective speed with Liquid/Mistral.
📖 Read the full source: r/LocalLLaMA
👀 See Also
NVIDIA announces NemoClaw with OpenShell security features
NVIDIA announced NemoClaw at GTC, building on OpenClaw to add enterprise-grade security through OpenShell, which enforces policy-based privacy and security guardrails for AI agents.
OpenClaw 5.2 Migration Breaks Cron Jobs and MCP Plugin Calls
Upgrading from OpenClaw 4.23 to 5.2 causes MCP tool plugins to be visible but not callable by the agent, and cron job registration via CLI fails with device pairing errors.
Claude Code 2.1.132: Multiagent docs, schedule gate, skill limit changes
Release v2.1.132 adds Managed Agents documentation for multiagent sessions, outcomes, and webhooks; introduces a default-deny gate for proactive /schedule offers; drops documented skill limit from 64 to 20 per agent.
Harmonic-9B: Two-stage Qwen3.5-9B fine-tune for AI agents
Developer DJLougen has released Harmonic-9B, a Qwen3.5-9B fine-tune optimized for agent use with a two-stage training approach. Stage 1 (heavy reasoning) is complete, while Stage 2 (light tool-calling) is still training. GGUF quantized versions are already available.