DeepSeek AI DeepSeek v3.1 vs Zai Org Glm 4.7
DeepSeek AI DeepSeek v3.1 (Together AI, 128,000-token context) versus Zai Org Glm 4.7 (Together AI, 200,000-token context). DeepSeek AI DeepSeek v3.1 is cheaper by 6% on a blended token mix. 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 — DeepSeek AI DeepSeek v3.1 vs Zai Org Glm 4.7
DeepSeek AI DeepSeek v3.1 and Zai Org Glm 4.7 are priced within 6% 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.
Zai Org Glm 4.7 ships a 200,000-token context window, 1.6x larger than DeepSeek AI DeepSeek v3.1's 128,000 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 128,000 tokens, the extra context on Zai Org Glm 4.7 is insurance you may never use — and DeepSeek AI DeepSeek v3.1 may win on other axes.
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-4-7
provider: together-ai
fallback:
model: deepseek-ai-deepseek-v3-1
provider: together-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| DeepSeek AI DeepSeek v3.1 | Zai Org Glm 4.7 | |
|---|---|---|
| Input price | $0.600/M | $0.450/M |
| Output price | $1.70/M | $2.00/M |
| Context window | 128,000 | 200,000 |
| Max output | 16,384 | 200,000 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | — |
| Structured output | — | — |
| Pricing verified | Jun 2, 2026 | Jun 2, 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 | DeepSeek AI DeepSeek v3.1 | Zai Org Glm 4.7 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $282 /mo | $255 /mo | $27.00/mo |
| Mid-market 100K requests/day | $2,820 /mo | $2,550 /mo | $270/mo |
| Enterprise 1M requests/day | $28,200 /mo | $25,500 /mo | $2,700/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.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 56% when moving from DeepSeek AI DeepSeek v3.1 (128,000) to Zai Org Glm 4.7 (200,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 16,384 on DeepSeek AI DeepSeek v3.1 vs 200,000 on Zai Org Glm 4.7. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
How to A/B test DeepSeek AI DeepSeek v3.1 vs Zai Org Glm 4.7 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 DeepSeek AI DeepSeek v3.1 primary, mirror 20% of traffic to Zai Org Glm 4.7 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 — DeepSeek AI DeepSeek v3.1 vs Zai Org Glm 4.7
Which is cheaper, DeepSeek AI DeepSeek v3.1 or Zai Org Glm 4.7? ▾
DeepSeek AI DeepSeek v3.1 is cheaper by roughly 6% on a blended input + output token mix. Input prices are $0.600/M for DeepSeek AI DeepSeek v3.1 versus $0.450/M for Zai Org Glm 4.7; output prices are $1.70/M versus $2.00/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 DeepSeek AI DeepSeek v3.1 versus Zai Org Glm 4.7? ▾
DeepSeek AI DeepSeek v3.1 supports up to 128,000 tokens of context. Zai Org Glm 4.7 supports up to 200,000 tokens. Zai Org Glm 4.7 has the larger window by a factor of 1.6x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do DeepSeek AI DeepSeek v3.1 and Zai Org Glm 4.7 both support tool calling? ▾
Yes — both DeepSeek AI DeepSeek v3.1 and Zai Org Glm 4.7 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.
How do I A/B test DeepSeek AI DeepSeek v3.1 against Zai Org Glm 4.7 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.