For most of 2026, GLM-5.2 has been the undisputed king of open-weight coding models. Zhipu AI's 753-billion-parameter MoE flagship topped the open-source leaderboard on SWE-bench Pro, Terminal-Bench 2.1, and the Artificial Analysis Intelligence Index, and it landed within striking distance of Claude Opus 4.8 on agentic benchmarks. Then, on July 31, DeepSeek dropped the 0731 update to V4 Flash — and the gap suddenly looked a lot narrower.
DeepSeek V4 Flash is the smaller, cheaper sibling in the V4 family: 284B total parameters (13B active) against GLM-5.2's 753B (40B active). It costs one-tenth the price of GLM-5.2 on the API ($0.14/$0.28 per million input/output tokens vs. $1.40/$4.40). And on several key coding benchmarks, the 0731 update doesn't just close the gap — it pulls ahead.
So did DeepSeek V4 Flash just dethrone GLM-5.2? The short answer is: it depends on the benchmark. This article walks through every major score, the pricing math, and where each model still holds a clear edge.
The 0731 update changed everything
The original DeepSeek V4 Flash preview (April 24, 2026) was a capable but clearly second-tier model: 61.8 on Terminal-Bench 2.1, 7.3 on DeepSWE, 37.0 on DSBench-FullStack. It was fast and cheap, but nobody confused it with a frontier model. The July 31 update (DeepSeek-V4-Flash-0731) is a different beast entirely. DeepSeek claims it "outperforms DeepSeek-V4-Pro (Preview) despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available."
The numbers back that up. On Terminal-Bench 2.1, Flash jumped from 61.8 to 82.7 — a 34% improvement that puts it ahead of GLM-5.2's 81.0. On DeepSWE, it went from 7.3 to 54.4, nearly matching GLM-5.2's 46.2. On DSBench-FullStack, it climbed from 37.0 to 68.7, beating GLM-5.2's 61.8. The 0731 update effectively turned a budget model into a contender.
DeepSeek V4 Flash vs. GLM-5.2 — key benchmark scores (%)
Where Flash wins: coding benchmarks
The chart above tells the story. DeepSeek V4 Flash leads on Terminal-Bench 2.1 (82.7 vs. 81.0) and DSBench-FullStack (68.7 vs. 61.8). These are real-world coding benchmarks: Terminal-Bench evaluates autonomous terminal-based development, and DSBench measures full-stack application building. On both, Flash's 0731 update edges out GLM-5.2 — remarkable for a model that costs one-tenth as much.
But SWE-bench Verified tells a different story. GLM-5.2 scores 82.8% on SWE-bench Verified — the benchmark that tests a model's ability to resolve real GitHub issues by editing code in unfamiliar repositories — while Flash lands at 79.0%. That's a 3.8-point lead for GLM-5.2 on the single most-cited coding benchmark in the field. Flash's SWE-bench Pro score (52.6%) also trails GLM-5.2's 62.1% by nearly 10 points. So the "dethroning" is real on Terminal-Bench and DSBench, but on the SWE-bench family GLM-5.2 still wears the crown.
Where GLM-5.2 still leads: reasoning and agentic work
GLM-5.2 isn't about to disappear. On the Artificial Analysis Intelligence Index, GLM-5.2 scores 51 vs. Flash's 50 (max effort) — a narrow but consistent lead on broad intelligence. On GPQA Diamond (graduate-level science reasoning), GLM-5.2 scores 91.2% vs. Flash's 88.1%. On AIME 2026 (math competition problems), GLM-5.2 hits 99.2% vs. Flash's 95.8%. And on MCP Atlas (tool-use and agentic capability), GLM-5.2 leads 76.8% to 69.0%.
The pattern is clear: when the task requires deep reasoning, multi-step tool use, or sustained agentic trajectories, GLM-5.2's larger active parameter count (40B vs. 13B) gives it an edge. Flash is remarkably efficient for its size, but it can't fully compensate for having 3× fewer active parameters on the hardest reasoning problems.
The price gap is enormous
This is where the comparison gets interesting. Here are the official API prices side by side:
| Model | Input / 1M tokens | Output / 1M tokens | Cached input | Cost per task* |
|---|---|---|---|---|
| DeepSeek V4 Flash | $0.14 | $0.28 | $0.0028 | ~$0.0005 |
| GLM-5.2 | $1.40 | $4.40 | $0.26 | ~$0.005 |
* Estimated per-task cost for a typical coding agent interaction (~8,000 input + ~1,000 output tokens, 50% cache hit rate).
Flash is 10× cheaper on input tokens and 15.7× cheaper on output tokens. The cached input gap is even wider: Flash's cache-hit rate of $0.0028/M is nearly 93× cheaper than GLM-5.2's $0.26/M. For a team running high-volume agentic workloads, the cost difference is transformative — Flash can handle roughly 10,000 coding agent tasks for the same price as 1,000 on GLM-5.2.
Full comparison table
| Specification | DeepSeek V4 Flash | GLM-5.2 |
|---|---|---|
| Developer | DeepSeek | Zhipu AI (Z.AI) |
| Release date | Apr 24, 2026 (0731: Jul 31) | Jun 13, 2026 |
| Parameters | 284B total / 13B active | 753B total / 40B active |
| Context window | 1M tokens | 1M tokens |
| Max output | 384K tokens | 128K tokens |
| Intelligence Index | 50 | 51 |
| SWE-bench Verified | 79.0% | 82.8% |
| SWE-bench Pro | 52.6% | 62.1% |
| Terminal-Bench 2.1 | 82.7 | 81.0 |
| GPQA Diamond | 88.1% | 91.2% |
| AIME 2026 | 95.8% | 99.2% |
| MCP Atlas | 69.0% | 76.8% |
| License | MIT | MIT |
| Open weights | Yes | Yes |
The verdict: dethroned or not?
If "dethroned" means "GLM-5.2 is no longer the best open-weight coding model," the answer is a qualified yes — on specific coding benchmarks. DeepSeek V4 Flash (0731) beats GLM-5.2 on Terminal-Bench 2.1 and DSBench-FullStack, all at one-tenth the price. For a team that primarily needs autonomous terminal-based development and full-stack app building, Flash is the better choice today.
If "dethroned" means "GLM-5.2 is obsolete," the answer is no. GLM-5.2 still leads on SWE-bench Verified (82.8% vs. 79.0%), SWE-bench Pro (62.1% vs. 52.6%), broad intelligence (Intelligence Index 51 vs. 50), graduate-level reasoning (GPQA Diamond 91.2% vs. 88.1%), math (AIME 99.2% vs. 95.8%), and agentic tool use (MCP Atlas 76.8% vs. 69.0%). For workloads that demand deep reasoning, repository-level code fixes, or long-horizon agentic trajectories, GLM-5.2 remains the stronger model.
The real story is that the open-weight landscape now has two genuinely competitive coding models at very different price points. GLM-5.2 is the premium option for the hardest reasoning tasks. DeepSeek V4 Flash is the high-value option for high-volume coding work. Both are MIT-licensed, both support 1M-token contexts, and both are within striking distance of closed frontier models. The "dethroning" narrative oversimplifies a market that's getting more interesting, not less.
The practical takeaway: if you're building a coding agent today, start with DeepSeek V4 Flash for the cost savings, and escalate to GLM-5.2 (or a closed frontier model) when the task requires deeper reasoning. That two-tier strategy is exactly the kind of workflow that's making open-weight models irresistible to the Fortune 500.