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// alternatives · October 2026

Pareto 26.9 alternatives

Cheaper models that come close, budget substitutes, and the cheapest step up — chosen on published LiveBench scores and live list price, never on our opinion. Every model here supports tool calling and image input, as Pareto 26.9 does.

LiveBench hasn't published a run for Pareto 26.9. The strongest benchmarked model at a similar or lower price is GPT-6.1 Sol, scoring 84.1 overall.

Pareto 26.9: no published LiveBench run, $3.75 per 1M blended tokens, about $1,550/mo for a RAG assistant.

Benchmarked models at or below its price

Pareto 26.9 has no published LiveBench run, so these are the highest-scoring benchmarked models priced no more than 25% above it — a known quantity to test against.

ModelOverallvs Pareto 26.9Blended / 1MRAG assistantContext
GPT-6.1 Solopenai

7% more expensive. Head to head →

84.1—$4.00$1,440/mo1.1M
Claude Sonnet 5.5anthropic

7% more expensive. Head to head →

80.0—$4.00$1,440/mo1M
Gemini 4 Argongoogle

7% more expensive. Head to head →

79.3—$4.00$1,440/mo1.0M
GPT-6.1 Terraopenai

47% cheaper. Head to head →

77.8—$2.00−47%$720.00/mo1.1M
DeepSeek V4.1 Flash 1017deepseek

86% cheaper. Head to head →

74.0—$0.525−86%$194.40/mo1.0M
Kimi K3.1 Promoonshotai

7% more expensive. Head to head →

71.3—$4.00$1,480/mo1.0M

Pareto 26.9 alternatives FAQ

What is the best alternative to Pareto 26.9?

GPT-6.1 Sol. 7% more expensive. Every suggestion supports tool calling and image input, like Pareto 26.9.

How alternatives are chosen

  • Scores — LiveBench release 2026-10-25. Only models with a published run are suggested on quality; a missing model means “not evaluated”, not “worse”.
  • Prices — live list prices from OpenRouter, blended 3:1 input:output. Monthly figures price a RAG assistant with the published cached-input rate where there is one.
  • Same job — suggestions must support every capability Pareto 26.9 has among tool calling and image input. Other listings of the same model (free or batch tiers) are variants, not alternatives, and are left out.

A few points on a public benchmark can hide large differences on your own tasks. Shortlist here, then decide with an eval — how to build one.