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Grok 4.20 vs Qwen2.5 VL 72B Instruct

Qwen2.5 VL 72B Instruct costs 2.5x less per output token than Grok 4.20 ($1.00 vs $2.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.

Grok 4.20xAI
Coding
33.3
Overall
41.0
Output $/M
$2.50
1
Qwen2.5 VL 72B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
0
vs boards won of 1 shared

Grok 4.20 or Qwen2.5 VL 72B Instruct: which to pick

Only Grok 4.20 has a coding score, 33.3 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding.

Pick Qwen2.5 VL 72B Instruct for output tokens 2.5x cheaper ($1.00 vs $2.50 per million).

Shared results
1
Grok 4.20 only
54
Qwen2.5 VL 72B Instruct only
5
Like-for-like categories
1 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
Grok 4.20 33.3 from 2 benchmarks, Qwen2.5 VL 72B Instruct not benchmarked.
Widest category
Reasoning, 39.0 points apart on average over 1 shared benchmark.
Widest single gap
Kagi LLM Benchmark: Grok 4.20 75.0% against Qwen2.5 VL 72B Instruct 36.0%, 39.0 points apart on a 0 to 100 scale.
Output price
Grok 4.20 $2.50 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 2.5x apart.
Context window
Grok 4.20 takes 2M tokens, Qwen2.5 VL 72B Instruct 128K.

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
CategoryGrok 4.20BarsQwen2.5 VL 72B InstructTies
Reasoning10
All shared boards10
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
Coding benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
LiveCodeBench (Vals AI)84.3%Not measuredOnly Grok 4.20 measured
SWE-bench Verified (Vals AI)72.2%Not measuredOnly Grok 4.20 measured
Vibe Code Bench (Vals AI)4.1%Not measuredOnly Grok 4.20 measured
Code migration (Vals AI)0.3%Not measuredOnly Grok 4.20 measured
Terminal-Bench 2.1 (Vals AI)44.2%Not measuredOnly Grok 4.20 measured
Terminal-Bench Hard (Artificial Analysis run)37.9%Not measuredOnly Grok 4.20 measured
Terminal-Bench57.3%Not measuredOnly Grok 4.20 measured
WeirdML52.3%Not measuredOnly Grok 4.20 measured
Neither model has a result on the other 31 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
AA Intelligence Index25.7Not measuredOnly Grok 4.20 measured
Epoch Capabilities Index152.1Not measuredOnly Grok 4.20 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Agentic work benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
Design Arena: agents building web apps1144Not measuredOnly Grok 4.20 measured
Design Arena: agents building full-stack apps1054Not measuredOnly Grok 4.20 measured
Design Arena: agents building mobile apps1090Not measuredOnly Grok 4.20 measured
Design Arena: agents building Android apps1045Not measuredOnly Grok 4.20 measured
Design Arena: agents building Godot games1089Not measuredOnly Grok 4.20 measured
Design Arena: HTML slides1180Not measuredOnly Grok 4.20 measured
Finance Agent (Vals AI)28.5%Not measuredOnly Grok 4.20 measured
Tax Agent Bench (Vals AI)7.0%Not measuredOnly Grok 4.20 measured
Legal Research Bench (Vals AI)13.9%Not measuredOnly Grok 4.20 measured
Excel Modeling Benchmark (Vals AI)11.7%Not measuredOnly Grok 4.20 measured
τ²-Bench Telecom93.0%Not measuredOnly Grok 4.20 measured
OSWorldNot measured5.0%Only Qwen2.5 VL 72B Instruct measured
Vending-Bench 2$4,663Not measuredOnly Grok 4.20 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
Reasoning benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
GPQA Diamond88.6%Not measuredOnly Grok 4.20 measured
MMMU Pro (Vals AI)83.5%Not measuredOnly Grok 4.20 measured
GPQA Diamond (Artificial Analysis run)91.1%Not measuredOnly Grok 4.20 measured
Humanity's Last Exam (Artificial Analysis run)34.5%Not measuredOnly Grok 4.20 measured
ARC-AGI89.5%Not measuredOnly Grok 4.20 measured
ARC-AGI-265.1%Not measuredOnly Grok 4.20 measured
Kagi LLM Benchmark75.0%36.0%Grok 4.20
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Math benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
AIME96.5%Not measuredOnly Grok 4.20 measured
Neither model has a result on the other 12 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Knowledge benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
MMLU Pro86.3%Not measuredOnly Grok 4.20 measured
LegalBench (Vals AI)77.7%Not measuredOnly Grok 4.20 measured
CaseLaw (Vals AI)54.4%Not measuredOnly Grok 4.20 measured
CorpFin (Vals AI)63.7%Not measuredOnly Grok 4.20 measured
TaxEval (Vals AI)74.1%Not measuredOnly Grok 4.20 measured
MortgageTax (Vals AI)45.4%Not measuredOnly Grok 4.20 measured
MedQA (Vals AI)94.5%Not measuredOnly Grok 4.20 measured
MedCode (Vals AI)32.2%Not measuredOnly Grok 4.20 measured
GeoBenchNot measured62.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
Design Arena: websites1229Not measuredOnly Grok 4.20 measured
Design Arena: UI components1200Not measuredOnly Grok 4.20 measured
Design Arena: game dev1193Not measuredOnly Grok 4.20 measured
Design Arena: data viz1217Not measuredOnly Grok 4.20 measured
Design Arena: 3D1193Not measuredOnly Grok 4.20 measured
Design Arena: all code categories1221Not measuredOnly Grok 4.20 measured
Design Arena: SVG1165Not measuredOnly Grok 4.20 measured
Design Arena: ASCII art1175Not measuredOnly Grok 4.20 measured
Neither model has a result on the other 1 board here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
General benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
SAGE (Vals AI)38.2%Not measuredOnly Grok 4.20 measured
MedScribe (Vals AI)63.4%Not measuredOnly Grok 4.20 measured
Vals Multimodal Index39.1%Not measuredOnly Grok 4.20 measured
IFBench81.2%Not measuredOnly Grok 4.20 measured
Long context reasoning (AA-LCR)69.0%Not measuredOnly Grok 4.20 measured
DTBench90.1%Not measuredOnly Grok 4.20 measured
LMCA38.7%Not measuredOnly Grok 4.20 measured
CL-bench22.2%Not measuredOnly Grok 4.20 measured
CL-bench Life11.9%Not measuredOnly Grok 4.20 measured
LMArena VisionNot measured1107Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: EnglishNot measured1115Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: ChineseNot measured1107Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 34 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Grok 4.20 and Qwen2.5 VL 72B Instruct
BenchmarkGrok 4.20Qwen2.5 VL 72B InstructLeader
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

Grok 4.20$0.0025Fits in one request
Qwen2.5 VL 72B Instruct$0.0013Fits in one request

Qwen2.5 VL 72B Instruct costs less

Repository review

50K input + 3K output tokens

Grok 4.20$0.070Fits in one request
Qwen2.5 VL 72B Instruct$0.043Fits in one request

Qwen2.5 VL 72B Instruct costs less

Agent loop

200K input + 10K output tokens

Grok 4.20$0.275Fits in one request
Qwen2.5 VL 72B Instruct$0.170Exceeds the context window

Qwen2.5 VL 72B Instruct 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 Grok 4.20 and Qwen2.5 VL 72B Instruct
SpecificationSpaceXAI Grok 4.20Qwen2.5 VL 72B Instruct
MakerxAIQwen
WeightsNo public weights listedOpen weights
ReleasedMarch 31, 2026February 1, 2025
Context window2M128K
Input $/Mtok$1.25$0.80
Output $/Mtok$2.50$1.00
Coding index33.3Not benchmarked
Independent measurements behind it20
Cost per solved task$0.72Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Grok 4.20 better than Qwen2.5 VL 72B Instruct for coding?

Only Grok 4.20 has a coding score, 33.3 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 VL 72B Instruct for output tokens 2.5x cheaper ($1.00 vs $2.50 per million).

Which is cheaper, Grok 4.20 or Qwen2.5 VL 72B Instruct?

Grok 4.20 costs $1.25 per million input tokens and $2.50 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for Grok 4.20 only.

Which has the bigger context window, Grok 4.20 or Qwen2.5 VL 72B Instruct?

Grok 4.20, with 2M tokens against 128K.

How many benchmarks is the Grok 4.20 vs Qwen2.5 VL 72B Instruct comparison based on?

1 benchmarks have a result for both models as of October 22, 2026. Grok 4.20's coding index rests on 2 independent benchmarks and Qwen2.5 VL 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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