Pairwise benchmark snapshot · Aug 17, 2026
In this head-to-head showdown, GLM-5.2 delivers higher overall intelligence and human-preferred responses (Elo 1,492 vs 1,490), while GLM-5.2 leads in SWE-bench software engineering benchmarks (69.3%), while Gemini 2.0 Flash Thinking is more budget-friendly at $0.4/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.
9 direct benchmark disciplines evaluated across capability, speed, and cost
Google experimental reasoning model that visualizes thoughts in real-time.
Zhipu flagship. Strong Chinese/English coding and agents.
Simulate accuracy gains vs added response time & token cost
Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.
Δ 2 Arena Elo pts
115 tok/s
$0.4 / 1M output
Relative percentile scores computed across all active models in the benchmark catalog.
Comparing top Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.
Category wins across reasoning intelligence, generation speed, and token cost.
Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy
Generation throughput and time to first token responsiveness
Cost per million tokens and max context window length
| Benchmark | Gemini 2.0 Flash Thinking | GLM-5.2 | Advantage Delta |
|---|---|---|---|
| Preference Elo | +2 | ||
| Coding Elo | +11 | ||
| LiveBench | +1.8% | ||
| SWE-bench | +2.5% | ||
| GPQA Diamond | +1.5% | ||
| Time to first token | +50 ms | ||
| Output speed | 115 tok/s | 84 tok/s | +31 tok/s |
| Input price | $0.1/1M | $1.19/1M | +$1.09/1M |
| Output price | $0.4/1M | $3.74/1M | +$3.34/1M |
| Context window | — |
Simulate monthly production API costs in USD (US Dollar).
Gemini 2.0 Flash Thinking is estimated to save $88.25/month ($1,059/year).
Higher SWE-bench (69.3%).
Lower output list price ($0.4/1M).
Lower TTFT (220 ms).
Larger window (1M).
Both accept images. Defaulting to the higher-Elo side (GLM-5.2).
| Target Workload | Recommended Pick | Evaluation Rationale |
|---|---|---|
| Repo / coding agents | GLM-5.2 | Higher SWE-bench (69.3%). |
| High-volume chat | Gemini 2.0 Flash Thinking | Lower output list price ($0.4/1M). |
| Voice / low-latency UI | Gemini 2.0 Flash Thinking | Lower TTFT (220 ms). |
| Long-document RAG | Gemini 2.0 Flash Thinking | Larger window (1M). |
| Screenshots / vision | GLM-5.2 | Both accept images. Defaulting to the higher-Elo side (GLM-5.2). |
GLM-5.2 undercuts GLM 5V Turbo by 74.2% on input at $0.31/1M, with 1,492 Elo and 69.3% SWE-bench in dated 2026 snapshots.
Strong Chinese/English coding. 5.3 is the new row; 5.2 stays so the delta is visible.
No comments posted on this matchup yet. Be the first to share an evaluation note!
Dynamic OpenGraph banner generated at /compare/gemini-2-flash-thinking-vs-glm-5-2/opengraph-image
Verified Head-to-Head Telemetry
