GLM 5.2vsOptimus Alpha
GLM 5.2 has a larger context window than Optimus Alpha (1.0M tokens vs 1.0M tokens).
| Spec | GLM 5.2 | Optimus Alpha |
|---|---|---|
| Input price | $1.4/M tokens | — |
| Output price | $4.4/M tokens | — |
| Context window | 1.0M tokens | 1.0M tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | — |
| Released | Jun 2026 | Apr 2025 |
Our Verdict
GLM 5.2
Optimus AlphaRunner-up
Not enough votes to call it. On the specs, GLM 5.2 has the edge: bigger model tier, newer, major provider backing.
Slight edge
Reviewing agent-written code?See a Brief PR report
API pricing
Cost per 1M tokens
GLM 5.2
Input
$1.40
Output
$4.40
Optimus Alpha
Input
—
Output
—
Where to run it
24 hosts, cheapest first
GLM 5.224 hosts
Baidu Qianfanfp8$0.48 in$1.51 out1M99.9% upStreamLakefp8$0.48 in$1.51 out1M99.4% upDeepInfrafp4$0.49 in$1.56 out1M99.4% upAmbientfp8$0.60 in$2.00 out203k98.2% upNovitafp8$0.68 in$2.15 out1M99.9% upDigitalOcean$0.70 in$2.20 out262k99.3% up
18 more hostsFewer hosts
CoreWeavefp4$0.76 in$2.42 out1M99.8% upAtlasCloudfp8$0.94 in$2.95 out1M99.9% upAlibaba Cloudfp8$0.97 in$3.04 out1M99.9% upInceptronfp4$1.08 in$2.89 out1M99.2% upSiliconFlowfp8$1.19 in$3.74 out1M100% upPhalafp8$1.26 in$3.00 out1M99.6% upBasetenfp8$1.40 in$4.40 out1M100% upCloudflare Workers AI$1.40 in$4.40 out262k100% upCrusoefp8$1.40 in$4.40 out1M98.1% upFireworks$1.40 in$4.40 out1M100% upFriendli$1.40 in$4.40 out1M99.9% upGMI Cloudfp8$1.40 in$4.40 out1M99.8% upMistral$1.40 in$4.40 out1M100% upParasailfp4$1.40 in$4.40 out262k99.9% upTogether$1.40 in$4.40 out512k98.6% upVenicefp8$1.40 in$4.40 out1M99.6% upZ.aifp8$1.40 in$4.40 out1M99.9% upDecartfp4$2.10 in$6.60 out1M99.8% up
Optimus Alpha
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Sep 2026.
Writing DNA
Style Comparison
Similarity
57%
Optimus Alpha uses 2.1x more bold
GLM 5.2
Optimus Alpha
56%Vocabulary63%
18wSentence Length18w
0.27Hedging0.55
3.7Bold7.7
2.8Lists5.7
0.00Emoji0.00
0.56Headings0.30
0.24Transitions0.27
Based on 27 + 10 text responses
Research
What we learned reading every model
FAQ
Common questions
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