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// alternatives · October 2026

GLM 5.3 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, as GLM 5.3 does.

LiveBench hasn't published a run for GLM 5.3. The strongest benchmarked model at a similar or lower price is Claude Haiku 5.5, scoring 69.8 overall.

GLM 5.3: no published LiveBench run, $0.402 per 1M blended tokens, about $139.52/mo for a RAG assistant.

Benchmarked models at or below its price

GLM 5.3 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 GLM 5.3Blended / 1MRAG assistantContext
Claude Haiku 5.5anthropic

50% cheaper. Head to head →

69.8—$0.200−50%$74.00/mo1M
MiniMax M3.1 Flashminimax

35% cheaper. Head to head →

66.9—$0.262−35%$102.00/mo1.0M
GPT-6 Lunaopenai

50% cheaper. Head to head →

64.6—$0.200−50%$74.00/mo1.1M
GLM 5.3 Flashz-ai

87% cheaper. Head to head →

62.1—$0.053−87%$34.59/mo1.0M
Qwen3.8 27Bqwen

15% cheaper. Head to head →

58.4—$0.342−15%$92.16/mo1M
Nemotron 3.5 Super VLnvidia

41% cheaper. Head to head →

50.2—$0.237−41%$98.00/mo1.0M

GLM 5.3 alternatives FAQ

What is the best alternative to GLM 5.3?

Claude Haiku 5.5. 50% cheaper. Every suggestion supports tool calling, like GLM 5.3.

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 GLM 5.3 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.