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Model comparison

Grok 4.20 vs Qwen3.6-27B

Data verified

Head-to-head evidence from 8 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

54.68/100
Margin
0.9pts
← winning
53.82/100
0 category wins3 category wins

Public leaderboard positions: Grok 4.20 #88 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Grok 4.20 and Qwen3.6-27B share 8 comparable benchmark results. 3 of 8 categories are comparable. 10 results are unique to Grok 4.20; 46 to Qwen3.6-27B.

Updated July 21, 2026
Shared results
8
Grok 4.20 only
10
Qwen3.6-27B only
46
Comparable categories
3 / 8

Pick Grok 4.20 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if agentic is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 8 shared benchmark results across 3 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Grok 4.20 has the cleaner BenchAlign overall profile here, landing at 54.68 versus 53.82. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Grok 4.20 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Grok 4.20 gives you the larger context window at 2M, compared with 262K for Qwen3.6-27B.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Grok 4.20 and Qwen3.6-27B
CategoryGrok 4.20ΔQwen3.6-27B
AgenticGrok 4.2047.1Margin→ 12.2Qwen3.6-27B59.3
CodingGrok 4.2067.1Margin→ 10.4Qwen3.6-27B77.5
MultimodalGrok 4.2070.1Margin→ 6.6Qwen3.6-27B76.7
ReasoningGrok 4.2053.3MarginNo overlapQwen3.6-27BNot measured
KnowledgeGrok 4.20Not measuredMarginNo overlapQwen3.6-27B53.3
MathGrok 4.20Not measuredMarginNo overlapQwen3.6-27B89.2

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Grok 4.20B · Qwen3.6-27B

  1. CharXiv

    Multimodal
    Source ↗
    A 60.9%B 78.4%
    Winner: Qwen3.6-27BΔ 17.5
    CharXiv: Grok 4.20 scored 60.9%; Qwen3.6-27B scored 78.4%. Qwen3.6-27B wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 47.1%B 59.3%
    Winner: Qwen3.6-27BΔ 12.2
    Terminal-Bench 2.0: Grok 4.20 scored 47.1%; Qwen3.6-27B scored 59.3%. Qwen3.6-27B wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 51.8%B 53.5%
    Winner: Qwen3.6-27BΔ 1.7
    SWE-bench Pro: Grok 4.20 scored 51.8%; Qwen3.6-27B scored 53.5%. Qwen3.6-27B wins this benchmark.

  4. MMMU-Pro

    Multimodal
    Source ↗
    A 75.2%B 75.8%
    Winner: Qwen3.6-27BΔ 0.6
    MMMU-Pro: Grok 4.20 scored 75.2%; Qwen3.6-27B scored 75.8%. Qwen3.6-27B wins this benchmark.

  5. SWE-bench Verified

    Coding
    Source ↗
    A 76.7%B 77.2%
    Winner: Qwen3.6-27BΔ 0.5
    SWE-bench Verified: Grok 4.20 scored 76.7%; Qwen3.6-27B scored 77.2%. Qwen3.6-27B wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGrok 4.20Qwen3.6-27BComparison
Input / output priceUSD per 1M tokensGrok 4.20$2 input / $6 outputQwen3.6-27B$0 input / $0 outputQwen3.6-27B has the lower combined listed price.
Generation speedtokens per secondGrok 4.20233 tok/sQwen3.6-27BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGrok 4.2010.33 sQwen3.6-27BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGrok 4.202MQwen3.6-27B262KGrok 4.20 lists the larger context window.

Benchmark Deep Dive

AgenticQwen3.6-27B wins
BenchmarkGrok 4.20Qwen3.6-27BResult
Terminal-Bench 2.0Source 47.1%59.3%Qwen3.6-27B leads
DeepSearchQASource 62.8%—Not comparable
Gert LabsSource 38.36%54.84%Qwen3.6-27B leads
Claw-EvalSource —72.4%Not comparable
QwenClawBenchSource —53.4%Not comparable
QwenWebBenchSource —1487Not comparable
AndroidWorldSource —70.3%Not comparable
AA Agentic IndexSource —27.0%Not comparable
τ²-bench resultsSource —94.2%Not comparable
GDPval-AASource —32.0%Not comparable
GDPval-AASource —1140Not comparable
CodingQwen3.6-27B wins
BenchmarkGrok 4.20Qwen3.6-27BResult
LiveCodeBench ProSource 74.2%—Not comparable
SWE-bench VerifiedSource 76.7%77.2%Qwen3.6-27B leads
SWE-bench ProSource 51.8%53.5%Qwen3.6-27B leads
Vibe Code BenchSource 4.06%—Not comparable
SWE MultilingualSource —71.3%Not comparable
Terminal-Bench 2.0Source —59.3%Not comparable
LiveCodeBenchSource —83.9%Not comparable
NL2RepoSource —36.2%Not comparable
AA Coding IndexSource —53.7%Not comparable
AA-SciCodeSource —39.8%Not comparable
Reasoning
BenchmarkGrok 4.20Qwen3.6-27BResult
ARC-AGI-2Source 53.3%—Not comparable
AA-LCRSource —68.7%Not comparable
CritPtSource —1.1%Not comparable
Knowledge
BenchmarkGrok 4.20Qwen3.6-27BResult
GPQA-DSource 88.5%—Not comparable
HLE w/o toolsSource 31.6%—Not comparable
HealthBench HardSource 20.3%—Not comparable
MedXpertQA (Text)Source 50.2%—Not comparable
MMLU-ProSource —86.2%Not comparable
MMLU-ReduxSource —93.5%Not comparable
SuperGPQASource —66%Not comparable
C-EvalSource —91.4%Not comparable
GPQASource —87.8%Not comparable
HLESource —24%Not comparable
Artificial Analysis Intelligence IndexSource —37.0%Not comparable
AA-GPQA DiamondSource —84.2%Not comparable
AA-HLESource —21.6%Not comparable
AA-Omniscience IndexSource —-19.8%Not comparable
AA-Omniscience AccuracySource —19.2%Not comparable
AA-Omniscience Hallucination RateSource —48.3%Not comparable
Math
BenchmarkGrok 4.20Qwen3.6-27BResult
HMMT Feb 2025Source —93.8%Not comparable
HMMT Nov 2025Source —90.7%Not comparable
HMMT Feb 2026Source —84.3%Not comparable
MMAnswerBenchSource —80.8%Not comparable
AIME26Source —94.1%Not comparable
MultimodalQwen3.6-27B wins
BenchmarkGrok 4.20Qwen3.6-27BResult
MMMU-ProSource 75.2%75.8%Qwen3.6-27B leads
CharXivSource 60.9%78.4%Qwen3.6-27B leads
ERQASource 54.1%62.5%Qwen3.6-27B leads
SimpleVQASource 57.4%56.1%Grok 4.20 leads
MedXpertQA (MM)Source 65.8%—Not comparable
Design Arena WebsiteSource 1257—Not comparable
MMMUSource —82.9%Not comparable
RealWorldQASource —84.1%Not comparable
DynaMathSource —85.6%Not comparable
MStarSource —81.4%Not comparable
CC-OCRSource —81.2%Not comparable
CountBenchSource —97.8%Not comparable
RefCOCO (avg)Source —92.5%Not comparable
Video-MME (with subtitle)Source —87.7%Not comparable
VideoMMMUSource —84.4%Not comparable
MLVU (M-Avg)Source —86.6%Not comparable
V*Source —94.7%Not comparable
AA-MMMU-ProSource —74.6%Not comparable
Inst. Following
BenchmarkGrok 4.20Qwen3.6-27BResult
AA-IFBenchSource —67.6%Not comparable
Frequently Asked Questions (4)

Which is better, Grok 4.20 or Qwen3.6-27B?

Grok 4.20 is ahead on BenchLM's BenchAlign leaderboard, 54.68 to 53.82. The biggest single separator in this matchup is CharXiv, where the scores are 60.9% and 78.4%.

Which is better for coding, Grok 4.20 or Qwen3.6-27B?

Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 67.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Grok 4.20 or Qwen3.6-27B?

Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 47.1. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.

Which is better for multimodal and grounded tasks, Grok 4.20 or Qwen3.6-27B?

Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.7 versus 70.1. Inside this category, CharXiv is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Grok 4.20
API / mo$6,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Model the full break-even

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