LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

GLM 4.7 vs MiMo-V2.5-Pro

Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace MiMo-V2.5-Pro
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
6
MiMo-V2.5-ProXiaomi
Coding
40.0
Overall
26.0
Output $/M
$0.609
52
vs boards won of 58 shared

Decision reading

Pick MiMo-V2.5-Pro for coding: it scores 40.0 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when what matters is LiveCodeBench (Vals AI) or τ²-Bench Telecom, where it leads.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BMiMo-V2.5-ProXiaomi40.0/1001 independent benchmark$0.03 per solved task
Shared results
58
GLM 4.7 only
22
MiMo-V2.5-Pro only
12
Like-for-like categories
8 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 4.7 33.6 from 1 benchmark, MiMo-V2.5-Pro 40.0 from 1 benchmark.
Widest category
Math, 12.9 points apart on average over 3 shared benchmarks.
Widest single gap
ALE-Bench: GLM 4.7 399.5 against MiMo-V2.5-Pro 899.8, 16.7 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against MiMo-V2.5-Pro $0.609 per million output tokens, 3.3x apart.
Context window
GLM 4.7 takes 205K tokens, MiMo-V2.5-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 4.7BarsMiMo-V2.5-ProTies
Coding19
Intelligence01
Agentic work10
Reasoning06
Math03
Knowledge27
Web design17
General119
All shared boards652
CodingLike-for-likeFour of these boards feed the Vibe Coding Index19
Coding benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
LMArena WebDev14351480MiMo-V2.5-Pro
AA Coding Index45.360.2MiMo-V2.5-Pro
LiveCodeBench (Vals AI)82.2%81.3%GLM 4.7
SWE-bench Verified (Vals AI)69.4%74.0%MiMo-V2.5-Pro
Terminal-Bench 4 (Vals AI)Not measured0.5%Only MiMo-V2.5-Pro measured
Vibe Code Bench (Vals AI)Not measured34.1%Only MiMo-V2.5-Pro measured
Code migration (Vals AI)Not measured21.6%Only MiMo-V2.5-Pro measured
Terminal-Bench 2.1 (Vals AI)Not measured57.3%Only MiMo-V2.5-Pro measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured50.6%Only MiMo-V2.5-Pro measured
Terminal-Bench Hard (Artificial Analysis run)31.8%43.2%MiMo-V2.5-Pro
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5899.8MiMo-V2.5-Pro
LMArena Text: coding14541501MiMo-V2.5-Pro
LMArena Text: software and IT14531495MiMo-V2.5-Pro
LMArena WebDev: React14201469MiMo-V2.5-Pro
LMArena WebDev: HTML14471473MiMo-V2.5-Pro
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
AA Intelligence Index22.226.0MiMo-V2.5-Pro
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
AA Agentic IndexNot measured21.3Only MiMo-V2.5-Pro measured
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
Finance Agent (Vals AI)Not measured41.5%Only MiMo-V2.5-Pro measured
Tax Agent Bench (Vals AI)Not measured11.5%Only MiMo-V2.5-Pro measured
Legal Research Bench (Vals AI)Not measured15.9%Only MiMo-V2.5-Pro measured
Excel Modeling Benchmark (Vals AI)Not measured55.2%Only MiMo-V2.5-Pro measured
τ²-Bench Telecom95.9%94.2%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
GPQA Diamond80.0%82.6%MiMo-V2.5-Pro
GPQA Diamond (Artificial Analysis run)85.9%86.6%MiMo-V2.5-Pro
Humanity's Last Exam (Artificial Analysis run)27.4%35.7%MiMo-V2.5-Pro
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431486MiMo-V2.5-Pro
LMArena Text: expert14241501MiMo-V2.5-Pro
LMArena Text: hard prompts in English14521494MiMo-V2.5-Pro
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%22.0%MiMo-V2.5-Pro
LMArena Text: math14241481MiMo-V2.5-Pro
LMArena Text: mathematical work14111491MiMo-V2.5-Pro
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index27
Knowledge benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
MMLU Pro82.7%84.6%MiMo-V2.5-Pro
LegalBench (Vals AI)83.4%77.1%GLM 4.7
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%61.4%MiMo-V2.5-Pro
TaxEval (Vals AI)68.8%73.8%MiMo-V2.5-Pro
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%32.5%GLM 4.7
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571482MiMo-V2.5-Pro
LMArena Text: medicine and healthcare14691473MiMo-V2.5-Pro
LMArena Text: legal and government14441474MiMo-V2.5-Pro
LMArena Text: business and finance14291466MiMo-V2.5-Pro
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index17
Web design benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
Design Arena: websites12261267MiMo-V2.5-Pro
Design Arena: UI components11991248MiMo-V2.5-Pro
Design Arena: game dev11891261MiMo-V2.5-Pro
Design Arena: data viz11951257MiMo-V2.5-Pro
Design Arena: 3D11911244MiMo-V2.5-Pro
Design Arena: all code categories12171266MiMo-V2.5-Pro
Design Arena: SVG11421181MiMo-V2.5-Pro
Design Arena: ASCII art11651157GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index119
General benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-ProLeader
MedScribe (Vals AI)68.6%83.7%MiMo-V2.5-Pro
IFBench67.9%79.9%MiMo-V2.5-Pro
Long context reasoning (AA-LCR)71.0%79.7%MiMo-V2.5-Pro
DTBenchNot measured84.5%Only MiMo-V2.5-Pro measured
LMCANot measured29.5%Only MiMo-V2.5-Pro measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351464MiMo-V2.5-Pro
LMArena Text: instruction following14121475MiMo-V2.5-Pro
LMArena Text: creative writing14011438MiMo-V2.5-Pro
LMArena Text: English14531477MiMo-V2.5-Pro
LMArena Text: non-English14171448MiMo-V2.5-Pro
LMArena Text: Chinese14951507MiMo-V2.5-Pro
LMArena Text: German14261459MiMo-V2.5-Pro
LMArena Text: Japanese14391410GLM 4.7
LMArena Text: Korean13991435MiMo-V2.5-Pro
LMArena Text: French14321487MiMo-V2.5-Pro
LMArena Text: Spanish14341469MiMo-V2.5-Pro
LMArena Text: Russian14231449MiMo-V2.5-Pro
LMArena Text: Polish14351464MiMo-V2.5-Pro
LMArena Text: multi-turn14461476MiMo-V2.5-Pro
LMArena Text: longer queries14321481MiMo-V2.5-Pro
LMArena Text: writing and language14111451MiMo-V2.5-Pro
LMArena Text: entertainment and media14001438MiMo-V2.5-Pro
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and MiMo-V2.5-Pro
BenchmarkGLM 4.7MiMo-V2.5-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 4.7$0.0015Fits in one request
MiMo-V2.5-Pro$0.0006Fits in one request

MiMo-V2.5-Pro costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
MiMo-V2.5-Pro$0.017Fits in one request

MiMo-V2.5-Pro costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
MiMo-V2.5-Pro$0.067Fits in one request

MiMo-V2.5-Pro 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 4.7 and MiMo-V2.5-Pro
SpecificationZ.ai GLM 4.7Xiaomi MiMo-V2.5-Pro
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025April 22, 2026
Context window205K1.05M
Input $/Mtok$0.54$0.3045
Output $/Mtok$1.98$0.609
Coding index33.640.0
Independent measurements behind it11
Cost per solved task$0.29$0.03
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than MiMo-V2.5-Pro for coding?

Pick MiMo-V2.5-Pro for coding: it scores 40.0 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when what matters is LiveCodeBench (Vals AI) or τ²-Bench Telecom, where it leads.

Which is cheaper, GLM 4.7 or MiMo-V2.5-Pro?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; MiMo-V2.5-Pro costs $0.3045 and $0.609. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and MiMo-V2.5-Pro $0.03.

Which has the bigger context window, GLM 4.7 or MiMo-V2.5-Pro?

MiMo-V2.5-Pro, with 1.05M tokens against 205K.

How many benchmarks is the GLM 4.7 vs MiMo-V2.5-Pro comparison based on?

58 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and MiMo-V2.5-Pro's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next