GLM 5.1 vs GLM 5.2
GLM 5.1 (Alibaba DashScope, 202,745-token context) versus GLM 5.2 (Alibaba DashScope, 1,048,576-token context). GLM 5.1 is cheaper by 0% on a blended token mix. Across 3 public benchmarks we tracked, GLM 5.1 wins 0 and GLM 5.2 wins 3. 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 — GLM 5.1 vs GLM 5.2
GLM 5.1 and GLM 5.2 are priced within 0% 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.
GLM 5.2 ships a 1,048,576-token context window, 5.2x larger than GLM 5.1's 202,745 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 202,745 tokens, the extra context on GLM 5.2 is insurance you may never use — and GLM 5.1 may win on other axes.
Across 3 public benchmarks, GLM 5.1 leads on 0 and GLM 5.2 leads on 3. The widest gap is on arena-webdev, where GLM 5.2 scores 96.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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: glm-5-2
provider: dashscope
fallback:
model: glm-5-1
provider: dashscope
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GLM 5.1 | GLM 5.2 | |
|---|---|---|
| Input price | $1.40/M | $1.40/M |
| Output price | $4.40/M | $4.40/M |
| Context window | 202,745 | 1,048,576 |
| Max output | 131,072 | 131,072 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 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 | GLM 5.1 | GLM 5.2 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $684 /mo | $684 /mo | — |
| Mid-market 100K requests/day | $6,840 /mo | $6,840 /mo | — |
| Enterprise 1M requests/day | $68,400 /mo | $68,400 /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 — GLM 5.2 fits 1,048,576 tokens versus the other model's 202,745, enough headroom for full books, large codebases, or 100+ page documents in one shot.
On arena-webdev, GLM 5.2 scores 96.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | GLM 5.1 | GLM 5.2 | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1465.0 | 1476.0 | GLM 5.2 | +11.0 |
| arena-elo-coding | 1512.0 | 1515.0 | GLM 5.2 | +3.0 |
| arena-webdev | 1508.0 | 1604.0 | GLM 5.2 | +96.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 417% when moving from GLM 5.1 (202,745) to GLM 5.2 (1,048,576). Re-check any prompt that relies on cramming long history or documents.
How to A/B test GLM 5.1 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 GLM 5.1 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 — GLM 5.1 vs GLM 5.2
What is the context window of GLM 5.1 versus GLM 5.2? ▾
GLM 5.1 supports up to 202,745 tokens of context. GLM 5.2 supports up to 1,048,576 tokens. GLM 5.2 has the larger window by a factor of 5.2x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GLM 5.1 and GLM 5.2 both support tool calling? ▾
Yes — both GLM 5.1 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.
Which model supports prompt caching for cost reduction? ▾
Both GLM 5.1 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 GLM 5.1 over GLM 5.2? ▾
On the data this page surfaces, GLM 5.1 is the right pick when GLM 5.2'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.
When should I choose GLM 5.2 over GLM 5.1? ▾
Your workload needs long context — GLM 5.2 fits 1,048,576 tokens versus the other model's 202,745, enough headroom for full books, large codebases, or 100+ page documents in one shot. On arena-webdev, GLM 5.2 scores 96.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test GLM 5.1 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.