Grok 4.20 Reasoning vs GLM 5.2 Maas
Grok 4.20 Reasoning (Google Vertex AI, 2,000,000-token context) versus GLM 5.2 Maas (Google Vertex AI, 1,000,000-token context). Grok 4.20 Reasoning is cheaper by 35% on a blended token mix. Grok 4.20 Reasoning uniquely supports vision input. 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 — Grok 4.20 Reasoning vs GLM 5.2 Maas
Grok 4.20 Reasoning and GLM 5.2 Maas target overlapping workloads but differ sharply on economics. Grok 4.20 Reasoning runs roughly 35% cheaper on a blended input-plus-output token mix, which translates to approximately $1,590 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Grok 4.20 Reasoning ships a 2,000,000-token context window, 2.0x 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 Grok 4.20 Reasoning is insurance you may never use — and GLM 5.2 Maas may win on other axes.
On capability surface area, the models diverge: Grok 4.20 Reasoning supports vision 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: xai-grok-4-20-reasoning
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| Grok 4.20 Reasoning | GLM 5.2 Maas | |
|---|---|---|
| Input price | $1.25/M | $1.40/M |
| Output price | $2.50/M | $4.40/M |
| Context window | 2,000,000 | 1,000,000 |
| Max output | 2,000,000 | 64,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
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 | Grok 4.20 Reasoning | GLM 5.2 Maas | Delta |
|---|---|---|---|
| Startup 10K requests/day | $525 /mo | $684 /mo | $159/mo |
| Mid-market 100K requests/day | $5,250 /mo | $6,840 /mo | $1,590/mo |
| Enterprise 1M requests/day | $52,500 /mo | $68,400 /mo | $15,900/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.
You're cost-sensitive at scale — Grok 4.20 Reasoning runs ~35% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Grok 4.20 Reasoning fits 2,000,000 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 — Grok 4.20 Reasoning accepts image input natively, the other doesn't.
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 Grok 4.20 Reasoning, switching to GLM 5.2 Maas means re-architecting that path (and vice versa).
- • Vision input
Capabilities both share (5)
- ✓ Function calling
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Prompt caching
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 50% when moving from Grok 4.20 Reasoning (2,000,000) to GLM 5.2 Maas (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 2,000,000 on Grok 4.20 Reasoning vs 64,000 on GLM 5.2 Maas. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Grok 4.20 Reasoning has capabilities GLM 5.2 Maas lacks: Vision input. Switching to GLM 5.2 Maas means re-architecting any flow that depends on these.
How to A/B test Grok 4.20 Reasoning 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 Grok 4.20 Reasoning 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 — Grok 4.20 Reasoning vs GLM 5.2 Maas
Which is cheaper, Grok 4.20 Reasoning or GLM 5.2 Maas? ▾
Grok 4.20 Reasoning is cheaper by roughly 35% on a blended input + output token mix. Input prices are $1.25/M for Grok 4.20 Reasoning versus $1.40/M for GLM 5.2 Maas; output prices are $2.50/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 Grok 4.20 Reasoning versus GLM 5.2 Maas? ▾
Grok 4.20 Reasoning supports up to 2,000,000 tokens of context. GLM 5.2 Maas supports up to 1,000,000 tokens. Grok 4.20 Reasoning has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Grok 4.20 Reasoning and GLM 5.2 Maas both support tool calling? ▾
Yes — both Grok 4.20 Reasoning 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 Grok 4.20 Reasoning and GLM 5.2 Maas process images? ▾
Grok 4.20 Reasoning accepts native image input. GLM 5.2 Maas does not — you would need to route image-heavy workloads through Grok 4.20 Reasoning or add a separate vision model in front of GLM 5.2 Maas.
Which model supports prompt caching for cost reduction? ▾
Both Grok 4.20 Reasoning and GLM 5.2 Maas support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose Grok 4.20 Reasoning over GLM 5.2 Maas? ▾
You're cost-sensitive at scale — Grok 4.20 Reasoning runs ~35% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — Grok 4.20 Reasoning fits 2,000,000 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 — Grok 4.20 Reasoning accepts image input natively, the other doesn't.
When should I choose GLM 5.2 Maas over Grok 4.20 Reasoning? ▾
On the data this page surfaces, GLM 5.2 Maas is the right pick when Grok 4.20 Reasoning's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Grok 4.20 Reasoning 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.