The frontier isn’t a model. It’s a router.

Fireworks argues that the effective frontier for coding agents may come from routing tasks among complementary models rather than relying on a single strongest model. Its analysis of 18 models on the 113-task DeepSWE v1.1 benchmark found that an hindsight-based “oracle” router could achieve a 97.6% success rate at an estimated $1.88 per task, compared with GPT-6 Astra’s 74.1% at $6.52, although the company notes that this oracle is upwardly biased because it selects winners after observing multiple outcomes. The analysis suggests that expensive models are uniquely best for relatively few tasks, while a curated portfolio of only a few models captures much of the potential gain, with the best three reaching 91.2% in the oracle evaluation. It also emphasizes that real-world routing is difficult because a router must predict the appropriate model before execution, and unreliable selection can be worse than consistently using one model. Fireworks presents FireRouter as a task-level, cache-aware routing system for open and closed models, reporting that its internal production coding traffic cost 53% less over four weeks than using Claude Opus 5 alone.

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