DeepSeek-V3.2 vs GLM-5
Wins 1 of 7 areas
Facts
Wins 5 of 7 areas
Coding · Agents · Math · Long documents · Following instructions
GLM-5 is the stronger all-rounder.DeepSeek-V3.2 is cheaper and better at facts.
Scores updated · 44 tests both models report · How we compare
Where each one wins
Tests won in each of the seven areas where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
- CodingWriting and fixing software05GLM-55 of 5 tests
- AgentsCarrying out multi-step tasks on its own03GLM-53 of 4 tests · 1 tie
- MathCompetition and research-level math12GLM-52 of 3 tests
- Long documentsFinding answers in very long texts01GLM-51 of 1 test
- Following instructionsDoing exactly what it is asked01GLM-51 of 1 test
- FactsGetting facts right instead of making them up21DeepSeek-V3.22 of 3 tests
- ReasoningHard problems that need careful thinking11Even1 each · 2 ties
Long documents and following instructions rest on a single test each.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
DeepSeek-V3.2 costs 83% less for the same work.
The biggest differences
The three tests each model wins by the widest margin. Scores are out of 100.
Where GLM-5 pulls ahead
- Avoids making up answers it doesn't knowAA-Omniscience · Non-hallucination+47.4points ahead
- Long, multi-file coding tasks in real codebasesSWE-bench Pro+39.5points ahead
- Real work tasks from 44 professionsGDPVal+34.8points ahead
Where DeepSeek-V3.2 pulls ahead
- Answers hard knowledge questions correctlyAA-Omniscience · Accuracy+6.7points ahead
- Graduate-level biology, physics and chemistry questionsGPQA Diamond+2points ahead
- Harvard-MIT high-school math contest problemsHMMT Feb. 2026+1.3points ahead
Every test, side by side
All 44 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingGLM-5
- SWE-bench ProGLM-5 by 39.515.655.1+39.5
- Terminal-Bench HardGLM-5 by 7.635.643.2+7.6
- SciCodeGLM-5 by 7.338.946.2+7.3
- LMArena · WebDevGLM-5 by 72 rating points13621434+72 rating
- SWE-bench VerifiedGLM-5 by 4.773.177.8+4.7
AgentsGLM-5
- GDPValGLM-5 by 34.89.844.6+34.8
- BrowseCompGLM-5 by 24.551.475.9+24.5
- MCP AtlasGLM-5 by 5.662.267.8+5.6
- AA ApexAgentstie14.514.5tie
ReasoningEven
- Humanity's Last ExamGLM-5 by 4.724.629.3+4.7
- GPQA DiamondDeepSeek-V3.2 by 28482+2
- ARC-AGI-2tie44.9tie
- CritPttie2.92tie
FactsDeepSeek-V3.2
- AA-Omniscience · Non-hallucinationGLM-5 by 47.417.364.7+47.4
- AA-Omniscience · AccuracyDeepSeek-V3.2 by 6.73326.3+6.7
- Vectara HHEM hallucination ratelower is betterDeepSeek-V3.2 by 3.86.310.1+3.8
MathGLM-5
- IMOAnswerBenchGLM-5 by 4.278.382.5+4.2
- HMMT Feb. 2026DeepSeek-V3.2 by 1.384.182.8+1.3
- AIME 2026GLM-5 by 1.294.295.4+1.2
Other results23 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- Vending-Bench 2GLM-5 by 3398 rating points10344432+3398 rating
- AA Agentic IndexGLM-5 by 44.818.363.1+44.8
- CyberGymGLM-5 by 3117.348.3+31
- AA-OmniscienceGLM-5 by 22.8-22.50.3+22.8
- swe_bench_bashGLM-5 by 12.86072.8+12.8
- vectara_avg_summary_lengthGLM-5 by 12.46274.4+12.4
- ARC-AGI-1DeepSeek-V3.2 by 12.35744.7+12.3
- Terminal-Bench 2.0GLM-5 by 9.846.456.2+9.8
- HLE (with tools)GLM-5 by 9.640.850.4+9.6
- t2-benchGLM-5 by 9.580.289.7+9.5
- τ²-BenchGLM-5 by 9.480.389.7+9.4
- browsecomp_with_context_managerGLM-5 by 8.367.675.9+8.3
- τ²-Bench Telecom (AA run)GLM-5 by 7.690.698.2+7.6
- vectara_answer_rateGLM-5 by 7.192.699.7+7.1
- HMMT Nov. 2025GLM-5 by 6.99096.9+6.9
- AA IntelligenceGLM-5 by 6.421.527.9+6.4
- vectara_factual_consistencyDeepSeek-V3.2 by 3.893.789.9+3.8
- AIME 2025GLM-5 by 3.693.196.7+3.6
- Tool-DecathlonGLM-5 by 2.835.238+2.8
- AIME 2026 I (Tools-allowed)tie92.792.7tie
- Artificial Analysis Coding Indextie44.244.2tie
- frontiermath_tier_4_v1tie2.12.1tie
- τ³-Benchtie69.269.2tie
Questions people ask
Which is better, DeepSeek-V3.2 or GLM-5?
GLM-5 wins five of the seven areas where both have results: coding, agents, math, long documents and following instructions. DeepSeek-V3.2 wins facts, and costs 83% less. They are level on reasoning.
Which is better for coding?
GLM-5. It wins 5 of the 5 coding tests both models report; DeepSeek-V3.2 wins none.
Which is cheaper?
DeepSeek-V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; GLM-5 costs $1.00 and $3.20. That makes DeepSeek-V3.2 about 83% cheaper for the same work.
How do you compare the two?
We use the 44 benchmark tests both models have published scores on. The verdict counts the 21 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 23 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.