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GLM 5.3 Flash vs MiMo-V2.6-Flash

Updated October 21, 2026. 60 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Flash Replace MiMo-V2.6-Flash
GLM 5.3 FlashZ.ai
Coding
71.0
Overall
41.8
Output $/M
$0.50
41
MiMo-V2.6-FlashXiaomi
Coding
61.9
Overall
37.9
Output $/M
$0.28
19
vs boards won of 60 shared

Decision reading

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 61.9 for MiMo-V2.6-Flash.

Pick MiMo-V2.6-Flash when output tokens cost 1.8x less, or when the context window is larger, 1.05M against 1.05M tokens.

Model AGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved taskModel BMiMo-V2.6-FlashXiaomi61.9/1001 independent benchmarkNo cost per solved task
Shared results
60
GLM 5.3 Flash only
42
MiMo-V2.6-Flash only
6
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 5.3 Flash 71.0 from 2 benchmarks, MiMo-V2.6-Flash 61.9 from 1 benchmark.
Widest category
Math, 16.5 points apart on average over 3 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5.3 Flash 30.8% against MiMo-V2.6-Flash 79.0%, 48.2 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against MiMo-V2.6-Flash $0.28 per million output tokens, 1.8x apart.
Context window
GLM 5.3 Flash takes 1.05M tokens, MiMo-V2.6-Flash 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 5.3 FlashBarsMiMo-V2.6-FlashTies
Coding510
Intelligence10
Agentic work34
Reasoning51
Math21
Knowledge40
General213
All shared boards4119
CodingLike-for-likeFour of these boards feed the Vibe Coding Index510
Coding benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev16071634MiMo-V2.6-Flash
AA Coding Index71.5Not measuredOnly GLM 5.3 Flash measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 Flash measured
SWE-bench Verified (Vals AI)92.0%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 4 (Vals AI)37.4%24.2%GLM 5.3 Flash
Vibe Code Bench (Vals AI)30.8%79.0%MiMo-V2.6-Flash
Code migration (Vals AI)20.5%40.9%MiMo-V2.6-Flash
ProgramBench (Vals AI)0.0%0.5%MiMo-V2.6-Flash
IOI (Vals AI)52.5%47.7%GLM 5.3 Flash
Terminal-Bench 2.1 (Vals AI)62.9%76.4%MiMo-V2.6-Flash
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%Not measuredOnly GLM 5.3 Flash measured
CyberBench (Vals AI)67.9%75.4%MiMo-V2.6-Flash
SciCode51.6%51.3%GLM 5.3 Flash
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
ALE-Bench303.61244.2MiMo-V2.6-Flash
LMArena Text: coding15071499GLM 5.3 Flash
LMArena Text: software and IT15001497GLM 5.3 Flash
LMArena WebDev: React16241648MiMo-V2.6-Flash
LMArena WebDev: HTML15771641MiMo-V2.6-Flash
LMArena WebDev: from an image15781596MiMo-V2.6-Flash
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
AA Intelligence Index41.837.9GLM 5.3 Flash
Epoch Capabilities Index153.1Not measuredOnly GLM 5.3 Flash measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index34
Agentic work benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
AA Agentic Index50.9Not measuredOnly GLM 5.3 Flash measured
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%4.2%MiMo-V2.6-Flash
Finance Agent (Vals AI)57.9%56.3%GLM 5.3 Flash
Tax Agent Bench (Vals AI)31.6%28.5%GLM 5.3 Flash
Legal Research Bench (Vals AI)45.2%38.0%GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)10.4%13.3%MiMo-V2.6-Flash
Excel Modeling Benchmark (Vals AI)55.9%65.5%MiMo-V2.6-Flash
BioMysteryBench (Vals AI)Not measured69.3%Only MiMo-V2.6-Flash measured
Terminal-Bench-Science (Vals AI)2.9%4.3%MiMo-V2.6-Flash
Vals Science IndexNot measured31.7%Only MiMo-V2.6-Flash measured
APEX-Agents52.8%Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
GPQA Diamond86.4%Not measuredOnly GLM 5.3 Flash measured
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MysteryMechanism (Vals AI)Not measured21.6%Only MiMo-V2.6-Flash measured
MMMU Pro (Vals AI)86.0%Not measuredOnly GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.2%Not measuredOnly GLM 5.3 Flash measured
Humanity's Last Exam (Artificial Analysis run)39.9%35.1%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
Chess puzzles21.0%Not measuredOnly GLM 5.3 Flash measured
Mystery game puzzles8.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts14901475GLM 5.3 Flash
LMArena Text: expert15111496GLM 5.3 Flash
LMArena Text: hard prompts in English14881493MiMo-V2.6-Flash
LMArena Vision: diagrams13171276GLM 5.3 Flash
LMArena Vision: homework13181283GLM 5.3 Flash
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
OTIS Mock AIME93.9%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tiers 1 to 355.8%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tier 417.1%Not measuredOnly GLM 5.3 Flash measured
ProofBench21.0%63.0%MiMo-V2.6-Flash
LMArena Text: math15031476GLM 5.3 Flash
LMArena Text: mathematical work14971478GLM 5.3 Flash
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
MMLU Pro86.1%Not measuredOnly GLM 5.3 Flash measured
LegalBench (Vals AI)83.9%Not measuredOnly GLM 5.3 Flash measured
TaxEval (Vals AI)75.6%Not measuredOnly GLM 5.3 Flash measured
MedCode (Vals AI)Not measured41.1%Only MiMo-V2.6-Flash measured
LMArena Text: science14841462GLM 5.3 Flash
LMArena Text: medicine and healthcare14751439GLM 5.3 Flash
LMArena Text: legal and government14731441GLM 5.3 Flash
LMArena Text: business and finance14601454GLM 5.3 Flash
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
Design Arena: websites1272Not measuredOnly GLM 5.3 Flash measured
Design Arena: UI components1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: game dev1279Not measuredOnly GLM 5.3 Flash measured
Design Arena: data viz1262Not measuredOnly GLM 5.3 Flash measured
Design Arena: 3D1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: all code categories1282Not measuredOnly GLM 5.3 Flash measured
Design Arena: SVG1284Not measuredOnly GLM 5.3 Flash measured
Design Arena: ASCII art1269Not measuredOnly GLM 5.3 Flash measured
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index213
General benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
Vals Index50.9%54.8%MiMo-V2.6-Flash
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
Public Benefits Bench (Vals AI)Not measured67.6%Only MiMo-V2.6-Flash measured
SAGE (Vals AI)Not measured43.5%Only MiMo-V2.6-Flash measured
MedScribe (Vals AI)88.9%85.3%GLM 5.3 Flash
Long context reasoning (AA-LCR)80.0%74.3%GLM 5.3 Flash
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
LMArena Text14711453GLM 5.3 Flash
LMArena Text: instruction following14781452GLM 5.3 Flash
LMArena Text: creative writing14411403GLM 5.3 Flash
LMArena Vision12941256GLM 5.3 Flash
LMArena Text: English14741476MiMo-V2.6-Flash
LMArena Text: non-English14621431GLM 5.3 Flash
LMArena Text: Chinese15311509GLM 5.3 Flash
LMArena Text: German14621458GLM 5.3 Flash
LMArena Text: Japanese14441359GLM 5.3 Flash
LMArena Text: Korean14361425GLM 5.3 Flash
LMArena Text: French14911480GLM 5.3 Flash
LMArena Text: Spanish14751453GLM 5.3 Flash
LMArena Text: Russian14651409GLM 5.3 Flash
LMArena Text: Polish14501455MiMo-V2.6-Flash
LMArena Text: multi-turn14691447GLM 5.3 Flash
LMArena Text: longer queries14811459GLM 5.3 Flash
LMArena Text: writing and language14551424GLM 5.3 Flash
LMArena Text: entertainment and media14401415GLM 5.3 Flash
LMArena Vision: English12951269GLM 5.3 Flash
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text12991270GLM 5.3 Flash
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing12701231GLM 5.3 Flash
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 Flash and MiMo-V2.6-Flash
BenchmarkGLM 5.3 FlashMiMo-V2.6-FlashLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5.3 Flash$0.0004Fits in one request
MiMo-V2.6-Flash$0.0003Fits in one request

MiMo-V2.6-Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
MiMo-V2.6-Flash$0.0078Fits in one request

MiMo-V2.6-Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
MiMo-V2.6-Flash$0.031Fits in one request

MiMo-V2.6-Flash costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5.3 Flash and MiMo-V2.6-Flash
SpecificationZ.ai GLM 5.3 FlashXiaomi MiMo-V2.6-Flash
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026September 21, 2026
Context window1.05M1.05M
Input $/Mtok$0.15$0.14
Output $/Mtok$0.50$0.28
Coding index71.061.9
Independent measurements behind it21
Cost per solved task$0.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than MiMo-V2.6-Flash for coding?

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 61.9 for MiMo-V2.6-Flash. Pick MiMo-V2.6-Flash when output tokens cost 1.8x less, or when the context window is larger, 1.05M against 1.05M tokens.

Which is cheaper, GLM 5.3 Flash or MiMo-V2.6-Flash?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; MiMo-V2.6-Flash costs $0.14 and $0.28. Cost per solved task is known for GLM 5.3 Flash only.

Which has the bigger context window, GLM 5.3 Flash or MiMo-V2.6-Flash?

MiMo-V2.6-Flash, with 1.05M tokens against 1.05M.

How many benchmarks is the GLM 5.3 Flash vs MiMo-V2.6-Flash comparison based on?

60 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and MiMo-V2.6-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

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