DeepSeek-V4.1-Flash vs GLM-5.2
DeepSeek-V4.1-Flash wins on Overall, Coding; GLM-5.2 wins on Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose DeepSeek-V4.1-Flash if you…
- 1M context at ~890 bytes/token global KV
- Top agentic coding (DeepSWE 74.2, Terminal-Bench 2.1 90.6)
- Persistent KV cut to ~1/8 via bounded replay
- Open weights, extremely low serving cost
- Best for: Long-context AI agents, Agentic coding, High-throughput deployment
Choose GLM-5.2 if you…
- Strong open-source ecosystem
- Good Chinese
- Mature enterprise services
- Best for: Chinese, Open source, Coding
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 27 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | DeepSeek-V4.1-Flash | GLM-5.2 | Verdict |
|---|---|---|---|
| Vendor | DeepSeek (CN) | Zhipu AI (CN) | Different vendors |
| Released | 2026.09 | 2026.06 | DeepSeek-V4.1-Flash is newer |
| Overall (rank) | 76 · #12 | 73 · #13 | DeepSeek-V4.1-Flash +3 |
| Coding | 89 · #5 | 66 · #20 | DeepSeek-V4.1-Flash +23 |
| Multimodal | 78 · #16 | 79 · #15 | GLM-5.2 +1 |
| Context window | 1M | 128K | DeepSeek-V4.1-Flash larger |
| Max output | 128K | 64K | GLM-5.2 longer |
| Effective-context | 98 | 88 | DeepSeek-V4.1-Flash more reliable |
| Input $/1M | $0.14 | $1.4 | DeepSeek-V4.1-Flash cheaper |
| Output $/1M | $0.28 | $4.4 | DeepSeek-V4.1-Flash cheaper |
| Cache discount | none | none | Tie |
| Speed | ~85 tok/s | ~50 tok/s | DeepSeek-V4.1-Flash faster |
| TTFT | 0.3s | 0.6s | DeepSeek-V4.1-Flash snappier |
| Function calling | 84 | 80 | DeepSeek-V4.1-Flash ahead |
| Refusal rate | ~5% | ~8% | DeepSeek-V4.1-Flash less restrictive |
| English | 84 | 70 | DeepSeek-V4.1-Flash |
| Chinese | 86 | 90 | GLM-5.2 |
| Modalities | text, image | text, image | Same |
| Open weights | Yes | Yes | Both open |
| Fine-tuning | Yes | Yes | |
| Free tier | DeepSeek App free; open weights to self-host | ChatGLM free; free quota on the open platform | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
Fields drawn from vendor public documentation and the Modelspectra 27-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
DeepSeek-V4.1-Flash leads the overall aggregate by 3 points (76 vs 73). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GLM-5.2 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a decisive gap: DeepSeek-V4.1-Flash scores 89 against 66. On multi-file edits, SWE-style tickets and long-horizon agent loops DeepSeek-V4.1-Flash needs fewer correction turns; GLM-5.2 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GLM-5.2 leads multimodal 79 vs 78. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
DeepSeek-V4.1-Flash is faster in interactive use: ~85 tok/s with 0.3s TTFT versus ~50 tok/s with 0.6s TTFT (about 1.7× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1M for DeepSeek-V4.1-Flash and 128K for GLM-5.2. Effective-context scores point the same way as window size — DeepSeek-V4.1-Flash is ahead on usable recall (98 vs 88), so prefer it for long-document work where details cannot be missed.
Price & total cost
DeepSeek-V4.1-Flash is the cheaper API at $0.14/$0.28 versus GLM-5.2 at $1.4/$4.4 per 1M input/output tokens — list input is about 10.0× lower.
Chinese vs English
English: DeepSeek-V4.1-Flash 84 vs GLM-5.2 70. Chinese: 86 vs 90. For Chinese-language production, GLM-5.2 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: DeepSeek-V4.1-Flash 84 vs GLM-5.2 80, so DeepSeek-V4.1-Flash has the edge on structured tool use. Fine-tuning is available from DeepSeek-V4.1-Flash and GLM-5.2. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | DeepSeek-V4.1-Flash | GLM-5.2 | Gap |
|---|---|---|---|
| List priceno cache applied | $22100M in × $0.14 + 30M out × $0.28 | $272100M in × $1.4 + 30M out × $4.4 | 12.36×gap |
| With caching90% of inputs cache-hit | $22no published cache discount | $272no published cache discount | 12.36×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.