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Composio
@composio
Your agent is smart. Its tools should be too. Check
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We tested DeepSeek V4 Flash against Kimi K3 and GLM 5.2 on 30 challenging agentic tasks. DeepSeek was 2.5x faster than GLM and 1.4x faster than Kimi at a similar success rate, despite using the most tokens per task. 🧵🧵🧵
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Hermes and Pi Agent led on the average cost per task, while Claude Code cost about 3.7x as much as Pi: - $0.39 Hermes Agent - $0.40 Pi Agent - $0.47 Codex - $0.51 OpenCode - $0.54 Kimi Code - $1.47 Claude Code The median cost tells the same story: $0.29 in Pi Agent and Hermes, $0.35 in OpenCode, $0.38 in Kimi Code, $0.39 in Codex and $0.72 in Claude Code, so the cost gap holds for a typical task and is not driven by a few expensive runs. We calculated these costs using Kimi K3’s list prices: $3/1M input tokens, $0.30/1M cached input tokens, and $15/1M output tokens.
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Lesson: If you want to reduce agent costs, examine the harness before switching models. In our data, the harness changed the cost by 9× while the model’s capabilities remained about the same.
As we mentioned, success rates stayed close, but speed and token efficiency split between harnesses: - Success rate: 22/28 for Kimi Code, 21/28 for Hermes, 20/28 for Claude Code - Median time per task: 179s in Hermes, 297s in Kimi Code, 348s in Claude Code So the fastest harness (Hermes) and the most token-efficient one (Kimi Code) were different.
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The median task consumed nearly 6x more tokens in Claude Code than in Kimi Code: - 61k in Kimi Code - 67k in Hermes - 340k in Claude Code At K3's $3/M input rate (input tokens make up roughly 95% of agentic workloads), the average cost per task was: - $0.22 in Kimi Code - $0.28 in Hermes - $2.00 in Claude Code
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We ran Kimi K3 through 3 agent harnesses (Claude Code, Hermes, Kimi Code) on 28 identical tasks. All 3 harnesses completed the tasks at similar success rates, but the interesting story is token efficiency: the same task cost up to 30x more tokens depending on the harness. 🧵🧵
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