Pairwise benchmark snapshot · Aug 17, 2026
In this head-to-head showdown, Gemini 3.6 Pro delivers higher overall intelligence and human-preferred responses (Elo 1,570 vs 1,528), while Gemini 3.6 Pro leads in SWE-bench software engineering benchmarks (74.6%), while Gemini 3.6 Pro is more budget-friendly at $5/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.
10 direct benchmark disciplines evaluated across capability, speed, and cost
Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.
Moonshot AI frontier flagship reasoning and agent swarm model with 256k context and top-tier SWE-bench coding capability.
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.
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 42 Arena Elo pts
102 tok/s
$5 / 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 3.6 Pro | Kimi K3 | Advantage Delta |
|---|---|---|---|
| Preference Elo | +42 | ||
| Coding Elo | +31 | ||
| LiveBench | +2.3% | ||
| SWE-bench | +0.8% | ||
| GPQA Diamond | +4% | ||
| Time to first token | +85 ms | ||
| Output speed | 102 tok/s | 68 tok/s | +34 tok/s |
| Input price | $1.25/1M | +$1.75/1M | |
| Output price | +$10/1M | ||
| Context window | +951k |
Simulate monthly production API costs in USD (US Dollar).
Gemini 3.6 Pro is estimated to save $211.25/month ($2,535/year).
Higher SWE-bench (74.6%).
Lower output list price ($5/1M).
Lower TTFT (205 ms).
Larger window (2M).
Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).
| Target Workload | Recommended Pick | Evaluation Rationale |
|---|---|---|
| Repo / coding agents | Gemini 3.6 Pro | Higher SWE-bench (74.6%). |
| High-volume chat | Gemini 3.6 Pro | Lower output list price ($5/1M). |
| Voice / low-latency UI | Gemini 3.6 Pro | Lower TTFT (205 ms). |
| Long-document RAG | Gemini 3.6 Pro | Larger window (2M). |
| Screenshots / vision | Gemini 3.6 Pro | Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro). |
3.7 Flash vs 3.6 Pro is the current Google volume-versus-upgrade pair.
Top Anthropic vs top Google. Usually Opus 5 vs 3.6 Pro. Two different invoices.
Hub remaps to top Google vs top OpenAI Elo. Usually 3.6 Pro vs Sol. Window vs price is the plot.
2M context, Pro-class Elo and coding. Upgrade path from Flash is this page, not a vibe check.
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