For coding agents, Gemini 3.5 Flash costs 2.1x as much as GLM 4.7 per request ($0.035 vs $0.017 on our measured traffic), because Gemini 3.5 Flash lists at $1.50 input, $0.15 cached input and $9 output per million tokens against $0.60, $0.11 and $2.20 for GLM 4.7.
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| Cost at list price | GLM 4.7 | Gemini 3.5 Flash |
|---|---|---|
| Prompt under 100K tokens50% of requests, 87% of input cached | $0.011 | $0.024 |
| Prompt of 100K to 272K39% of requests, 88% of input cached | $0.026 | $0.052 |
| Prompt over 272K tokens11% of requests, 86% of input cached | n/a | n/a |
| Average request | $0.017 | $0.035 |
| 100 requests | $1.68 | $3.46 |
| Median session16 requests; a session ends after 10 idle minutes | $0.19 | $0.41 |
| A month of regular usemedian of 20 users active on 5+ days | $17.73 | $39.23 |
Each logged request priced at the listed rates above for its own token counts, including cache reads and any higher rate for long prompts. Average output: 698 tokens per request. Both models are priced on the requests up to 205K tokens that fit in both context windows; band shares above describe all requests.
| Artificial Analysis | GLM 4.7 | Gemini 3.5 Flash |
|---|---|---|
| Intelligence Index10 evaluations, 0 to 100 | 22 (estimate) | 33 |
| Terminal-Bench 4.0agentic coding in a terminal | n/a | 6.6% |
| Output speedmedian | 42 tokens/s | 197 tokens/s |
| Time to first answer tokenincludes thinking time | 49 s | 17 s |
Source: Artificial Analysis (artificialanalysis.ai). Measured variants: GLM-4.7 (Reasoning); Gemini 3.5 Flash (High), speed on Google. Fetched October 13, 2026. Artificial Analysis runs Terminal-Bench 4.0 in its own setup, so its scores differ from the tbench.ai leaderboard.
GLM 4.7's 205K window cannot take 20% of coding-agent requests, so the comparison uses only requests both can take. Gemini 3.5 Flash is superseded: Gemini 3.8 Flash costs $0.048 per request. The price is OpenRouter's listed rate on October 13, 2026, which can be a third-party host's; Google's own API may charge differently.
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Correctness and depth of what it produces
Tool calls, instruction-following, and multi-step tasks
Tokens per second and time-to-first-token
How much capability you get per dollar
Consistent results: fewer refusals, loops and format breaks
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Current Claude, GPT, Gemini and open models run on Standard Compute