Kimi K3 vs GLM 5.2
Kimi K3 (Novita AI, 1,048,576-token context) versus GLM 5.2 (DeepInfra, 1,048,576-token context). GLM 5.2 is cheaper by 83% on a blended token mix. Kimi K3 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 — Kimi K3 vs GLM 5.2
Kimi K3 and GLM 5.2 target overlapping workloads but differ sharply on economics. GLM 5.2 runs roughly 83% cheaper on a blended input-plus-output token mix, which translates to approximately $14,310 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.
On capability surface area, the models diverge: Kimi K3 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: zai-org-glm-5-2
provider: deepinfra
fallback:
model: moonshotai-kimi-k3
provider: novita-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Kimi K3 | GLM 5.2 | |
|---|---|---|
| Input price | $3.00/M | $0.750/M |
| Output price | $15.00/M | $2.40/M |
| Context window | 1,048,576 | 1,048,576 |
| Max output | 1,048,576 | — |
| 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 | Kimi K3 | GLM 5.2 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,800 /mo | $369 /mo | $1,431/mo |
| Mid-market 100K requests/day | $18,000 /mo | $3,690 /mo | $14,310/mo |
| Enterprise 1M requests/day | $180,000 /mo | $36,900 /mo | $143,100/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 — GLM 5.2 runs ~83% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your inputs include screenshots, diagrams, or product photos — Kimi K3 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 Kimi K3, switching to GLM 5.2 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.
- Kimi K3 has capabilities GLM 5.2 lacks: Vision input. Switching to GLM 5.2 means re-architecting any flow that depends on these.
- Provider changes from Novita AI to DeepInfra. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Kimi K3 vs GLM 5.2 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 Kimi K3 primary, mirror 20% of traffic to GLM 5.2 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 — Kimi K3 vs GLM 5.2
Which is cheaper, Kimi K3 or GLM 5.2? ▾
GLM 5.2 is cheaper by roughly 83% on a blended input + output token mix. Input prices are $3.00/M for Kimi K3 versus $0.750/M for GLM 5.2; output prices are $15.00/M versus $2.40/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 Kimi K3 versus GLM 5.2? ▾
Kimi K3 supports up to 1,048,576 tokens of context. GLM 5.2 supports up to 1,048,576 tokens. GLM 5.2 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Kimi K3 and GLM 5.2 both support tool calling? ▾
Yes — both Kimi K3 and GLM 5.2 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 Kimi K3 and GLM 5.2 process images? ▾
Kimi K3 accepts native image input. GLM 5.2 does not — you would need to route image-heavy workloads through Kimi K3 or add a separate vision model in front of GLM 5.2.
Which model supports prompt caching for cost reduction? ▾
Both Kimi K3 and GLM 5.2 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 Kimi K3 over GLM 5.2? ▾
Your inputs include screenshots, diagrams, or product photos — Kimi K3 accepts image input natively, the other doesn't.
When should I choose GLM 5.2 over Kimi K3? ▾
You're cost-sensitive at scale — GLM 5.2 runs ~83% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
How do I A/B test Kimi K3 against GLM 5.2 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.