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// alternatives · October 2026

GPT-6.1 Terra 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 GPT-6.1 Terra does.

The closest cheaper alternative to GPT-6.1 Terra is DeepSeek V4.1 Flash 1017 — 3.8 points lower overall, 74% cheaper on LiveBench and list price.

GPT-6.1 Terra: 77.8 overall on LiveBench, $2.00 per 1M blended tokens, about $720.00/mo for a RAG assistant.

Cheaper, close in quality

Lower list price and within 5 points of GPT-6.1 Terra on LiveBench overall — the switches least likely to be noticed by users. Strongest first.

ModelOverallvs GPT-6.1 TerraBlended / 1MRAG assistantContext
DeepSeek V4.1 Flash 1017deepseek

3.8 points lower overall, 74% cheaper. Head to head →

74.0−3.8$0.525−74%$194.40/mo1.0M

Budget substitutes

Within 15 points of GPT-6.1 Terra, cheapest first — and only models that are the best score at their price, so nothing here is beaten by a cheaper pick. Worth testing for high-volume traffic where the last few points matter less than the bill.

ModelOverallvs GPT-6.1 TerraBlended / 1MRAG assistantContext
Claude Haiku 5.5anthropic

7.9 points lower overall, 90% cheaper. Head to head →

69.8−7.9$0.200−90%$74.00/mo1M

Match it on one task for less

For each LiveBench category, the cheapest model that scores at least as well as GPT-6.1 Terra there. Useful when your workload is mostly one kind of task.

CategoryCheapest matchScoreBlended / 1M
CodingDeepSeek V4.1 Flash 101796.0 vs 94.3$0.525−74%
ReasoningDeepSeek V4.1 Flash 101789.1 vs 88.7$0.525−74%
ResearchClaude Haiku 5.581.9 vs 73.9$0.200−90%
Documents & designClaude Haiku 5.583.1 vs 82.3$0.200−90%

Stronger options, cheapest first

Models that beat GPT-6.1 Terra on LiveBench overall, ordered by what the upgrade costs.

ModelOverallvs GPT-6.1 TerraBlended / 1MRAG assistantContext
Gemini 4 Argongoogle

1.5 points higher overall, 100% more expensive. Head to head →

79.3+1.5$4.00$1,440/mo1.0M
GPT-6.1 Solopenai

6.3 points higher overall, 100% more expensive. Head to head →

84.1+6.3$4.00$1,440/mo1.1M
Claude Sonnet 5.5anthropic

2.2 points higher overall, 100% more expensive. Head to head →

80.0+2.2$4.00$1,440/mo1M
Claude Opus 5.5anthropic

7.4 points higher overall, 300% more expensive. Head to head →

85.1+7.4$8.00$2,880/mo1M
GPT-6 Astraopenai

7.8 points higher overall, 900% more expensive. Head to head →

85.6+7.8$20.00$7,400/mo1.1M

GPT-6.1 Terra alternatives FAQ

What is the best alternative to GPT-6.1 Terra?

DeepSeek V4.1 Flash 1017. 3.8 points lower overall, 74% cheaper. Every suggestion supports tool calling and image input, like GPT-6.1 Terra.

What is the cheapest alternative to GPT-6.1 Terra?

Claude Haiku 5.5 at $0.200 per 1M blended tokens, scoring 69.8 overall against GPT-6.1 Terra's 77.8. On a RAG assistant at 100K requests a month that is $74.00 instead of $720.00.

Which model is better than GPT-6.1 Terra?

The cheapest model that scores higher overall on LiveBench is Gemini 4 Argon — 1.5 points higher overall, 100% more expensive.

Is there a cheaper model that matches GPT-6.1 Terra on specific tasks?

Yes, in 4 of the seven LiveBench categories. For example, coding: DeepSeek V4.1 Flash 1017 (96.0 vs 94.3); reasoning: DeepSeek V4.1 Flash 1017 (89.1 vs 88.7); research: Claude Haiku 5.5 (81.9 vs 73.9).

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 GPT-6.1 Terra 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.