Gemini 1.5 Pro vs GLM 5.2 Maas
Gemini 1.5 Pro (Google Vertex AI, 2,097,152-token context) versus GLM 5.2 Maas (Google Vertex AI, 1,000,000-token context). GLM 5.2 Maas is cheaper by 7% on a blended token mix. Gemini 1.5 Pro uniquely supports parallel tool calls and vision input. GLM 5.2 Maas uniquely supports prompt caching and native reasoning mode. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Gemini 1.5 Pro vs GLM 5.2 Maas
Gemini 1.5 Pro and GLM 5.2 Maas are priced within 7% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Gemini 1.5 Pro ships a 2,097,152-token context window, 2.1x larger than GLM 5.2 Maas's 1,000,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 1,000,000 tokens, the extra context on Gemini 1.5 Pro is insurance you may never use — and GLM 5.2 Maas may win on other axes.
On capability surface area, the models diverge: Gemini 1.5 Pro supports parallel tool calls where the other does not; Gemini 1.5 Pro supports vision input where the other does not; Gemini 1.5 Pro supports pdf input where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gemini-1-5-pro
provider: vertex-ai
fallback:
model: zai-org-glm-5-2-maas
provider: vertex-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Gemini 1.5 Pro | GLM 5.2 Maas | |
|---|---|---|
| Input price | $1.25/M | $1.40/M |
| Output price | $5.00/M | $4.40/M |
| Context window | 2,097,152 | 1,000,000 |
| Max output | 8,192 | 64,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | — | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | May 7, 2026 | Oct 22, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Gemini 1.5 Pro | GLM 5.2 Maas | Delta |
|---|---|---|---|
| Startup 10K requests/day | $675 /mo | $684 /mo | $9.00/mo |
| Mid-market 100K requests/day | $6,750 /mo | $6,840 /mo | $90.00/mo |
| Enterprise 1M requests/day | $67,500 /mo | $68,400 /mo | $900/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Gemini 1.5 Pro fits 2,097,152 tokens versus the other model's 1,000,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Gemini 1.5 Pro accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — GLM 5.2 Maas ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
You re-send the same large system prompt across requests — GLM 5.2 Maas supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Gemini 1.5 Pro, switching to GLM 5.2 Maas means re-architecting that path (and vice versa).
- • Parallel tool calls
- • Vision input
- • PDF input
- • Prompt caching
- • Native reasoning mode
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Structured output (JSON schema)
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 52% when moving from Gemini 1.5 Pro (2,097,152) to GLM 5.2 Maas (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on Gemini 1.5 Pro vs 64,000 on GLM 5.2 Maas. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Gemini 1.5 Pro has capabilities GLM 5.2 Maas lacks: Parallel tool calls, Vision input, PDF input. Switching to GLM 5.2 Maas means re-architecting any flow that depends on these.
- GLM 5.2 Maas has capabilities Gemini 1.5 Pro lacks: Prompt caching, Native reasoning mode. Worth wiring through the agent design before commit.
- Pricing on Gemini 1.5 Pro was last verified 180 days ago — confirm against the provider's published rate card before committing to a multi-month migration.
How to A/B test Gemini 1.5 Pro vs GLM 5.2 Maas in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Gemini 1.5 Pro primary, mirror 20% of traffic to GLM 5.2 Maas in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Gemini 1.5 Pro vs GLM 5.2 Maas
Which is cheaper, Gemini 1.5 Pro or GLM 5.2 Maas? ▾
GLM 5.2 Maas is cheaper by roughly 7% on a blended input + output token mix. Input prices are $1.25/M for Gemini 1.5 Pro versus $1.40/M for GLM 5.2 Maas; output prices are $5.00/M versus $4.40/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Gemini 1.5 Pro versus GLM 5.2 Maas? ▾
Gemini 1.5 Pro supports up to 2,097,152 tokens of context. GLM 5.2 Maas supports up to 1,000,000 tokens. Gemini 1.5 Pro has the larger window by a factor of 2.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Gemini 1.5 Pro and GLM 5.2 Maas both support tool calling? ▾
Yes — both Gemini 1.5 Pro and GLM 5.2 Maas support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Gemini 1.5 Pro and GLM 5.2 Maas process images? ▾
Gemini 1.5 Pro accepts native image input. GLM 5.2 Maas does not — you would need to route image-heavy workloads through Gemini 1.5 Pro or add a separate vision model in front of GLM 5.2 Maas.
Which model supports prompt caching for cost reduction? ▾
GLM 5.2 Maas supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GLM 5.2 Maas gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Gemini 1.5 Pro over GLM 5.2 Maas? ▾
Your workload needs long context — Gemini 1.5 Pro fits 2,097,152 tokens versus the other model's 1,000,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Gemini 1.5 Pro accepts image input natively, the other doesn't.
When should I choose GLM 5.2 Maas over Gemini 1.5 Pro? ▾
Your tasks involve multi-step planning or math-heavy reasoning — GLM 5.2 Maas ships a native reasoning mode that explicitly thinks before responding, the other doesn't. You re-send the same large system prompt across requests — GLM 5.2 Maas supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Gemini 1.5 Pro against GLM 5.2 Maas in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.