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GLM 5.3 Flash vs MiMo-V2.6-Pro

MiMo-V2.6-Pro leads GLM 5.3 Flash on coding, 65.4 to 57.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 1.7x less per output token ($0.50 vs $0.87 per million). Updated October 22, 2026; 67 benchmarks have a result for both.

Replace GLM 5.3 Flash Replace MiMo-V2.6-Pro
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
13
MiMo-V2.6-ProXiaomi
Coding
65.4
Overall
68.4
Output $/M
$0.87
53
vs boards won of 67 shared, 1 tied

GLM 5.3 Flash or MiMo-V2.6-Pro: which to pick

Pick MiMo-V2.6-Pro for coding: it scores 65.4 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6.

Pick GLM 5.3 Flash for output tokens 1.7x cheaper ($0.50 vs $0.87 per million) and work measured by Terminal-Bench 4 (Vals AI) or IOI (Vals AI), where it leads.

Model AGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved taskModel BMiMo-V2.6-ProXiaomi65.4/1004 independent benchmarksNo cost per solved task
Shared results
67
GLM 5.3 Flash only
35
MiMo-V2.6-Pro only
6
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 57.7 from 6 benchmarks, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
Widest category
Math, 17.3 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-Pro 85.2%, 54.5 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against MiMo-V2.6-Pro $0.87 per million output tokens, 1.7x apart.
Context window
GLM 5.3 Flash takes 1.05M tokens, MiMo-V2.6-Pro 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-ProTies
Coding214
Intelligence01
Agentic work191 tie
Reasoning25
Math12
Knowledge04
Web design07
General711
All shared boards13531 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index214
Coding benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
LiveBench coding80.6%81.5%MiMo-V2.6-Pro
LMArena WebDev16071625MiMo-V2.6-Pro
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%31.3%GLM 5.3 Flash
Vibe Code Bench (Vals AI)30.8%85.2%MiMo-V2.6-Pro
Code migration (Vals AI)20.5%43.0%MiMo-V2.6-Pro
ProgramBench (Vals AI)0.0%0.5%MiMo-V2.6-Pro
IOI (Vals AI)52.5%39.3%GLM 5.3 Flash
Terminal-Bench 2.1 (Vals AI)62.9%67.8%MiMo-V2.6-Pro
SWE-bench Multimodal (Vals AI)Not measured41.3%Only MiMo-V2.6-Pro measured
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%72.9%MiMo-V2.6-Pro
SciCode51.6%60.9%MiMo-V2.6-Pro
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
ALE-Bench303.61158.1MiMo-V2.6-Pro
LMArena Text: coding15071533MiMo-V2.6-Pro
LMArena Text: software and IT15001528MiMo-V2.6-Pro
LMArena WebDev: React16241639MiMo-V2.6-Pro
LMArena WebDev: HTML15771616MiMo-V2.6-Pro
LMArena WebDev: from an image15781598MiMo-V2.6-Pro
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
AA Intelligence Index41.846.3MiMo-V2.6-Pro
Epoch Capabilities Index153.1Not measuredOnly GLM 5.3 Flash measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index19
Agentic work benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
AA Agentic Index50.9Not measuredOnly GLM 5.3 Flash measured
Design Arena: agents building web appsNot measured1290Only MiMo-V2.6-Pro measured
LiveBench research70.1%76.7%MiMo-V2.6-Pro
LiveBench browser84.0%88.0%MiMo-V2.6-Pro
LiveBench long agentic28.4%47.5%MiMo-V2.6-Pro
SREBench (Vals AI)0.8%3.0%MiMo-V2.6-Pro
Finance Agent (Vals AI)57.9%57.3%GLM 5.3 Flash
Tax Agent Bench (Vals AI)31.6%32.4%MiMo-V2.6-Pro
Legal Research Bench (Vals AI)45.2%47.1%MiMo-V2.6-Pro
Harvey's Legal Agent Benchmark (Vals AI)10.4%13.8%MiMo-V2.6-Pro
Excel Modeling Benchmark (Vals AI)55.9%62.9%MiMo-V2.6-Pro
Terminal-Bench-Science (Vals AI)2.9%2.9%Tie
APEX-Agents52.8%54.4%MiMo-V2.6-Pro
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index25
Reasoning benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
GPQA Diamond86.4%Not measuredOnly GLM 5.3 Flash measured
LiveBench reasoning77.6%79.6%MiMo-V2.6-Pro
MysteryMechanism (Vals AI)Not measured15.3%Only MiMo-V2.6-Pro 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%49.4%MiMo-V2.6-Pro
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 prompts14901511MiMo-V2.6-Pro
LMArena Text: expert15111541MiMo-V2.6-Pro
LMArena Text: hard prompts in English14881520MiMo-V2.6-Pro
LMArena Vision: diagrams13171281GLM 5.3 Flash
LMArena Vision: homework13181264GLM 5.3 Flash
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index12
Math benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
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%70.0%MiMo-V2.6-Pro
LMArena Text: math15031500GLM 5.3 Flash
LMArena Text: mathematical work14971511MiMo-V2.6-Pro
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
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 measured45.0%Only MiMo-V2.6-Pro measured
LMArena Text: science14841506MiMo-V2.6-Pro
LMArena Text: medicine and healthcare14751497MiMo-V2.6-Pro
LMArena Text: legal and government14731485MiMo-V2.6-Pro
LMArena Text: business and finance14601468MiMo-V2.6-Pro
Neither model has a result on the other 10 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
Design Arena: websites12721308MiMo-V2.6-Pro
Design Arena: UI components13161340MiMo-V2.6-Pro
Design Arena: game dev12781314MiMo-V2.6-Pro
Design Arena: data viz12621336MiMo-V2.6-Pro
Design Arena: 3D13161330MiMo-V2.6-Pro
Design Arena: all code categories12821317MiMo-V2.6-Pro
Design Arena: SVG1285Not measuredOnly GLM 5.3 Flash measured
Design Arena: ASCII art1267Not measuredOnly GLM 5.3 Flash measured
LiveBench design77.2%78.5%MiMo-V2.6-Pro
GeneralLike-for-likePublic boards, shown as published, kept out of the index711
General benchmarks for GLM 5.3 Flash and MiMo-V2.6-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
Vals Index50.9%57.8%MiMo-V2.6-Pro
LiveBench data analysis76.4%72.3%GLM 5.3 Flash
Public Benefits Bench (Vals AI)Not measured68.9%Only MiMo-V2.6-Pro measured
SAGE (Vals AI)Not measured45.0%Only MiMo-V2.6-Pro measured
MedScribe (Vals AI)88.9%88.3%GLM 5.3 Flash
Long context reasoning (AA-LCR)80.0%86.3%MiMo-V2.6-Pro
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
LMArena Text14711491MiMo-V2.6-Pro
LMArena Text: instruction following14781491MiMo-V2.6-Pro
LMArena Text: creative writing14411466MiMo-V2.6-Pro
LMArena Vision12941263GLM 5.3 Flash
LMArena Text: English14741504MiMo-V2.6-Pro
LMArena Text: non-English14621474MiMo-V2.6-Pro
LMArena Text: Chinese15311529GLM 5.3 Flash
LMArena Text: German1462Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Japanese1444Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Korean1436Not measuredOnly GLM 5.3 Flash measured
LMArena Text: French1491Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Spanish1475Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Russian14651479MiMo-V2.6-Pro
LMArena Text: Polish1450Not measuredOnly GLM 5.3 Flash measured
LMArena Text: multi-turn14691466GLM 5.3 Flash
LMArena Text: longer queries14811501MiMo-V2.6-Pro
LMArena Text: writing and language14551470MiMo-V2.6-Pro
LMArena Text: entertainment and media14401465MiMo-V2.6-Pro
LMArena Vision: English12951268GLM 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 writing1270Not measuredOnly GLM 5.3 Flash measured
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-Pro
BenchmarkGLM 5.3 FlashMiMo-V2.6-ProLeader
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-Pro$0.0009Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
MiMo-V2.6-Pro$0.024Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
MiMo-V2.6-Pro$0.096Fits in one request

GLM 5.3 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-Pro
SpecificationZ.ai GLM 5.3 FlashXiaomi MiMo-V2.6-Pro
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026September 21, 2026
Context window1.05M1.05M
Input $/Mtok$0.15$0.435
Output $/Mtok$0.50$0.87
Coding index57.765.4
Independent measurements behind it64
Cost per solved task$0.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Pick MiMo-V2.6-Pro for coding: it scores 65.4 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash, though on 4 benchmarks against 6. Pick GLM 5.3 Flash for output tokens 1.7x cheaper ($0.50 vs $0.87 per million) and work measured by Terminal-Bench 4 (Vals AI) or IOI (Vals AI), where it leads.

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

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. 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-Pro?

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

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

67 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and MiMo-V2.6-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.

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<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-flash-vs-xiaomi-mimo-v2-6-pro" title="GLM 5.3 Flash vs MiMo-V2.6-Pro, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-3-flash-vs-xiaomi-mimo-v2-6-pro">LLMBenchmark.ai, GLM 5.3 Flash vs MiMo-V2.6-Pro</a></p>