Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.7-Flash (Reasoning) | Gemma 4 26B A4B (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.07 | $0.13 |
| Output ($/M tokens) | $0.4 | $0.4 |
Verdict. GLM-4.7-Flash (Reasoning) wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Gemma 4 26B A4B (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Gemma 4 26B A4B (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.7-Flash (Reasoning) is strongest on Intelligence Index (14.9). Gemma 4 26B A4B (Non-reasoning) leads on Intelligence Index (13.1).
Speed. On throughput, GLM-4.7-Flash (Reasoning) generates tokens at 77 tok/s versus 49 tok/s — about 37% faster. On time-to-first-token, Gemma 4 26B A4B (Non-reasoning) responds in 1390ms vs 1580ms, which matters most for chat-style UIs.
Provider. Z AI and Google sell to overlapping but distinct developer audiences: Z AI tends to ship frontier reasoning models with premium positioning, while Google often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): GLM-4.7-Flash (Reasoning) costs $8.10 ($97/year); Gemma 4 26B A4B (Non-reasoning) costs $9.90 ($119/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.7-Flash (Reasoning) ≈ $1.15/run, Gemma 4 26B A4B (Non-reasoning) ≈ $1.45/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.7-Flash (Reasoning) ≈ $54/run, Gemma 4 26B A4B (Non-reasoning) ≈ $66/run. GLM-4.7-Flash (Reasoning) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
GLM-4.7-Flash (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.15/M tokens vs $0.20/M for Gemma 4 26B A4B (Non-reasoning).
GLM-4.7-Flash (Reasoning) wins 1 out of 12 benchmarks compared to 0 for Gemma 4 26B A4B (Non-reasoning). See the detailed benchmark chart above for per-category results.
GLM-4.7-Flash (Reasoning) generates tokens faster at 77 tok/s vs 49 tok/s. However, Gemma 4 26B A4B (Non-reasoning) has lower time-to-first-token (1.39s vs 1.58s).
Choose based on your priorities: GLM-4.7-Flash (Reasoning) for lower cost, GLM-4.7-Flash (Reasoning) for stronger benchmark performance, and GLM-4.7-Flash (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.