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  5. GLM 4.6vsQwen3 235B A22B 2507
Updated Sep 2025

GLM 4.6vsQwen3 235B A22B 2507

Qwen3 235B A22B 2507 is cheaper than GLM 4.6 at $0.00015/M vs $0.4/M input tokens.

GLM 4.6 and Qwen3 235B A22B 2507 compared across 36 shared prompts
SpecGLM 4.6Qwen3 235B A22B 2507
Input price$0.4/M tokens$0.00015/M tokens
Output price$1.75/M tokens$0.00085/M tokens
Context window203K tokens—
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedSep 2025Jul 2025
Our Verdict
GLM 4.6
GLM 4.6
Qwen3 235B A22B 2507
Qwen3 235B A22B 2507Runner-up

Not enough votes to call it. On the specs, GLM 4.6 has the edge: newer, bigger context window, major provider backing.

GLM 4.6 wins Reasoning and Image Generation. Qwen3 235B A22B 2507 costs 2059x less per token.

Slight edge
API pricing

Cost per 1M tokens

GLM 4.6
Input
$0.40
Output
$1.75
Qwen3 235B A22B 2507
Input
$0.000
2667× cheaper
Output
$0.001
2059× cheaper

Qwen3 235B A22B 2507 is cheaper on both: 2667× input, 2059× output.

Where to run it

15 hosts, cheapest first

GLM 4.65 hosts
HostInOutContextUptime
VVenicefp4$0.43 in·$1.75 out·198k·99.8% upDDeepInfrafp4$0.50 in·$2.00 out·203k·99.9% upNNovitabf16$0.55 in·$2.20 out·205k·95.1% upAAtlasCloudfp8degraded$0.60 in·$2.20 out·203k·84.5% upZ.aifp4degraded$0.60 in·$2.20 out·203k·92.3% up
Qwen3 235B A22B 250710 hosts
HostInOutContextUptime
GGMI Cloudfp8$0.09 in·$0.35 out·262k·97.4% upDDeepInfrafp8$0.09 in·$0.55 out·262k·93.4% upNNovitafp8$0.09 in·$0.58 out·131k·97.9% upPParasailfp8$0.14 in·$0.80 out·131k·99.6% upAlibaba Cloud$0.15 in·$0.60 out·131k·100% upAAtlasCloudfp8$0.20 in·$0.88 out·131k·99% up
4 more hostsFewer hosts
SStreamLake$0.21 in·$0.84 out·128k·98.7% upGoogle Vertex AI$0.22 in·$0.88 out·262k·99.7% upVVenicefp8degraded$0.15 in·$0.75 out·128k·92.7% upNNebiusfp8degraded$0.20 in·$0.60 out·262k·76.8% up

Per million tokens. Prices and uptime via OpenRouter, checked 9 Sep 2026.

Writing DNA

Style Comparison

Similarity
50%

Qwen3 235B A22B 2507 uses 3.2x more emoji

GLM 4.6
Qwen3 235B A22B 2507
52%Vocabulary57%
16wSentence Length18w
0.31Hedging0.40
5.1Bold5.9
3.2Lists4.8
0.14Emoji0.45
0.54Headings1.07
0.08Transitions0.11
Based on 28 + 18 text responses
Research

What we learned reading every model

FAQ

Common questions

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