Model comparison

Grok 4.7 vs Qwen3 14B

Grok 4.7 is the stronger model overall, scoring 46.6 to 35.0 on the Noometry Index. Qwen3 14B costs 4.9× less per token, which makes it the better buy when Grok 4.7's lead doesn't matter for your workload.

Last verified October 10, 2026. 2 shared benchmarks.

Grok 4.7 xAI

46.6

Rank #56 Confirmed

Qwen3 14B

Alibaba (Qwen)

35.0

Rank #222 Confirmed

Summary

They share 2 benchmarks with published results for both. Grok 4.7 scores higher in 4 categories and Qwen3 14B in 1 category; 4 gaps are clear of the uncertainty.
The widest gap is in reasoning, where Grok 4.7 leads 40.1 to 18.1.
The biggest single-benchmark swing is SciCode: 57.4% for Grok 4.7 and 31.6% for Qwen3 14B.
Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $2 / $6 for Grok 4.7.
Grok 4.7 accepts more context: 500K tokens versus 131K.
Qwen3 14B has downloadable open weights; the other is API-only.

Side by side

Grok 4.7 and Qwen3 14B specifications
	Grok 4.7	Qwen3 14B
Provider	xAI	Alibaba (Qwen)
Noometry Index	46.6	35.0
Released	2026-09-21	2025-04
Weights	Proprietary	Open
Context window	500K	131K
Max output	500K	8K
Input $ / M tokens	$2	$0.35
Output $ / M tokens	$6	$1.40
Results tracked	21	12

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Category by category

Coding Grok 4.7 leads

Grok 4.7: 55.0 (#23), Qwen3 14B: 36.7 (#191)

Coding benchmarks
Benchmark	Grok 4.7	Qwen3 14B
SciCode	57.4%	31.6%
CursorBench	46.3%	—
LMArena WebDev	1636	—
FrontierSWE	29.5%	—
LMArena Coding	1423	—

Agentic & Tool Use Not comparable

Grok 4.7: —, Qwen3 14B: 29.4 (#78)

Agentic & Tool Use benchmarks
Benchmark	Grok 4.7	Qwen3 14B
Berkeley Function Calling Leaderboard	—	41%

Reasoning Grok 4.7 leads

Grok 4.7: 40.1 (#49), Qwen3 14B: 18.1 (#272)

Reasoning benchmarks
Benchmark	Grok 4.7	Qwen3 14B
CritPt	17.7%	0%
Kagi LLM Benchmark	—	49.1%
NYT Connections (extended)	76.8%	—
Chess Puzzles	—	4%
LMArena Hard Prompts	1409	—
DTBench	—	64%
LMCA	—	18.2%
Epoch Capabilities Index	—	138.23

Math Grok 4.7 leads

Grok 4.7: 41.1 (#73), Qwen3 14B: 37.0 (#102)

Math benchmarks
Benchmark	Grok 4.7	Qwen3 14B
OTIS Mock AIME 2024-2025	—	66.4%
ProofBench	34%	—
LMArena Math	1393	—

Knowledge Too close to call

Grok 4.7: 38.7 (#130), Qwen3 14B: 39.2 (#124)

Knowledge benchmarks
Benchmark	Grok 4.7	Qwen3 14B
GPQA Diamond	—	63.8%
Vectara Hallucination Rate	—	5.4%
LMArena Expert	1413	—

Multimodal Not comparable

Grok 4.7: 28.8 (#107), Qwen3 14B: —

Multimodal benchmarks
Benchmark	Grok 4.7	Qwen3 14B
Furniture Assembly	20.8%	—

Multilingual Not comparable

Grok 4.7: 51.2 (#108), Qwen3 14B: —

Multilingual benchmarks
Benchmark	Grok 4.7	Qwen3 14B
LMArena Non-English	1392	—
LMArena Chinese	1476	—
LMArena Russian	1396	—

Instruction Following Not comparable

Grok 4.7: 74.3 (#98), Qwen3 14B: —

Instruction Following benchmarks
Benchmark	Grok 4.7	Qwen3 14B
LMArena Instruction Following	1406	—

Long Context Grok 4.7 leads

Grok 4.7: 43.2 (#97), Qwen3 14B: 38.0 (#201)

Long Context benchmarks
Benchmark	Grok 4.7	Qwen3 14B
Fiction.LiveBench	—	62.5%
LMArena Longer Query	1416	—

Writing & Preference Not comparable

Grok 4.7: 70.1 (#23), Qwen3 14B: —

Writing & Preference benchmarks
Benchmark	Grok 4.7	Qwen3 14B
LMArena Text	1397	—
LMArena Creative Writing	1391	—
EQ-Bench Creative Writing	2007	—
LMArena Multi-Turn	1389	—

Frequently asked questions

Is Grok 4.7 better than Qwen3 14B?

Which is cheaper, Grok 4.7 or Qwen3 14B?

Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Grok 4.7 lists at $2 and $6.

Is Grok 4.7 or Qwen3 14B better for coding?

Grok 4.7 scores higher on coding benchmarks: 55.0 versus 36.7 in the Noometry coding category.

Which has the bigger context window?

Grok 4.7 does, with 500K tokens against 131K.

How many benchmarks do Grok 4.7 and Qwen3 14B share?

2 benchmarks have published results for both models. Grok 4.7 has 21 scored results on Noometry and Qwen3 14B has 12.