Cheaper alternatives to Olmo 3 32B Think

Allen AI LMArena 1298.5 $0.237/M on a balanced workload prices as of 2026-08-28

11 models are both better and cheaper, giving up nothing.

Of the 133 models carrying both an independent score and a published price at the same delivery mode, 22 score at least as high as Olmo 3 32B Think and cost no more under at least one workload. 11 match or beat it on every capability we hold — context, maximum output, input modes, tool use and extended reasoning. 11 do not, and every row names what it drops.

Dominance is not a property of a model. It is a property of a model, a capability lens and a workload, and all three are named on every row. Scores are LMArena Elo, used under CC BY 4.0 from the official dataset; prices are blended per million tokens. A higher score is not a drop-in replacement.

This page is built from that comparison and nothing else. When no model is both better and cheaper than Olmo 3 32B Think, it is not generated.

Where it holds

WorkloadOlmo 3 32B Think $/MBetter and cheaperWith a trade
Balanced$0.23789
Summarise$0.168910
Chat$0.29099
Code gen$0.360108
Agentic$0.2031010

Workload mixes are defined on the methodology page. Cached input is priced at the cached rate, and reasoning tokens at the worse of the reasoning and output rates.

What you would be replacing

Intelligence1298.5
Context window66K
Max output59K
Input modestext
Tool useno
Extended reasoningyes

A replacement has to clear every line above, not just the score. Full record for Olmo 3 32B Think.

Better and cheaper, nothing given up 11

Each of these matches or beats Olmo 3 32B Think on context, maximum output, input modes, tool use and reasoning, scores at least as high, and costs no more.

#ModelIntelligence$/MHolds under
1Hy3 Tencent1441.2 +142.7$0.144
  • Balanced −39%
  • Summarise −54%
  • Chat −44%
  • Code gen −37%
  • Agentic −59%
2DeepSeek V4 Flash 0423 DeepSeek1431.6 +133.1$0.101
  • Balanced −57%
  • Summarise −60%
  • Chat −68%
  • Code gen −65%
  • Agentic −73%
3MiMo-V2.5 Xiaomi1427.3 +128.8$0.175
  • Balanced −26%
  • Summarise −36%
  • Chat −47%
  • Code gen −41%
  • Agentic −61%
4Step 3.5 Flash StepFun1403.8 +105.3$0.150
  • Balanced −37%
  • Summarise −34%
  • Chat −38%
  • Code gen −39%
  • Agentic −36%
5Qwen3.5-Flash Qwen1397.6 +99.1$0.114
  • Balanced −52%
  • Summarise −55%
  • Chat −51%
  • Code gen −49%
  • Agentic −53%
6Solar Pro 4 Upstage1376.2 +77.7$0.052
  • Balanced −78%
  • Summarise −83%
  • Chat −80%
  • Code gen −77%
  • Agentic −86%
7gpt-oss-120b OpenAI1365.6 +67.1$0.070
  • Balanced −70%
  • Summarise −74%
  • Chat −69%
  • Code gen −68%
  • Agentic −72%
8GPT-5 Nano OpenAI1320.3 +21.8$0.138
  • Balanced −42%
  • Summarise −67%
  • Chat −39%
  • Code gen −29%
  • Agentic −63%
9DeepSeek V3.2 DeepSeek1424.6 +126.1$0.269 chat
  • Chat −7%
  • Code gen −11%
  • Agentic −1%
10DeepSeek V3.2 Exp DeepSeek1424.4 +125.9$0.354 code gen
  • Code gen −2%
11GLM 4.5 Air Z.ai1382.8 +84.3$0.136 summarise
  • Summarise −19%
  • Agentic −13%

Cheaper and higher-scoring, but you give something up 11

These score at least as high and cost no more on the two plotted axes, and lose something that is not on them. Read the last column before switching.

ModelIntelligence$/MHolds underWhat you give up
Gemma 4 31B Google1441.7 +143.2$0.152
  • Balanced −36%
  • Summarise −46%
  • Chat −39%
  • Code gen −34%
  • Agentic −49%
  • 59K → 16K max output
Gemma 4 26B A4B Google1434.6 +136.1$0.138
  • Balanced −42%
  • Summarise −50%
  • Chat −39%
  • Code gen −36%
  • Agentic −45%
  • 59K → 16K max output
Qwen3 235B A22B Instruct 2507 Qwen1419.3 +120.8$0.205
  • Balanced −14%
  • Summarise −33%
  • Chat −6%
  • Agentic −21%
  • 59K → 16K max output
  • no extended reasoning
Qwen3 30B A3B Instruct 2507 Qwen1384.3 +85.8$0.084
  • Balanced −64%
  • Summarise −67%
  • Chat −63%
  • Code gen −62%
  • Agentic −65%
  • 59K → 32K max output
  • no extended reasoning
Gemma 3 27B Google1358.3 +59.8$0.172
  • Balanced −27%
  • Summarise −48%
  • Chat −26%
  • Code gen −17%
  • Agentic −45%
  • no extended reasoning
GLM 4.7 Flash Z.ai1352.9 +54.4$0.145
  • Balanced −39%
  • Summarise −63%
  • Chat −38%
  • Code gen −28%
  • Agentic −60%
  • 59K → 16K max output
Qwen3 32B Qwen1340.1 +41.6$0.130
  • Balanced −45%
  • Summarise −46%
  • Chat −45%
  • Code gen −44%
  • Agentic −46%
  • 59K → 16K max output
Gemma 3 12B Google1334.2 +35.7$0.075
  • Balanced −68%
  • Summarise −67%
  • Chat −69%
  • Code gen −69%
  • Agentic −68%
  • 59K → 16K max output
  • no extended reasoning
Qwen3 30B A3B Qwen1316.9 +18.4$0.215
  • Balanced −9%
  • Summarise −17%
  • Chat −6%
  • Code gen −3%
  • Agentic −13%
  • 59K → 16K max output
Qwen3 Next 80B A3B Instruct Qwen1418.6 +120.1$0.141 summarise
  • Summarise −16%
  • no extended reasoning
Mercury 2 Inception1357.8 +59.3$0.191 agentic
  • Agentic −6%
  • 59K → 50K max output

What this compares, and what it leaves out

  • Quality is LMArena Elo, used under CC BY 4.0 from the official dataset. The 95% confidence interval on a difference between two scores is about ±10.68 points, so a gap smaller than that is marked tie rather than an improvement — see significance bands.
  • 188 further models at this delivery mode carry a price but no independent score. They are absent from the comparison in both directions — unrated is not a zero, and an unmeasured model is neither an alternative nor a worse buy.
  • Retired models are never offered as an alternative, and a model only competes against its own delivery mode: batch trades latency for price, so it is not a like-for-like swap.
  • Nothing here measures latency, throughput, rate limits or how a model behaves on your prompts. Two models with the same index score are not interchangeable.
  • The models that nothing beats on both axes are on the value frontier, and every other model something cheaper beats is listed here.