Quick Verdict
If you only read one section, read this one. These two models are close enough that the right answer is a workload question, not a quality question.
Choose GLM 5.2 for…
Agentic coding, tool use, and balanced frontier work. It tops SWE-Bench Pro at 68.5% and SWE-Verified at 84%, holds the higher Arena ELO (1565), and — critically — has a Mac-runnable Air variant. It is the better default for most builders shipping real software.
Choose DeepSeek R3 for…
Pure mathematical and chain-of-thought reasoning. It leads AIME at 95% and scales accuracy further when you grant longer thinking budgets. If your workload is proofs, competition math, or deep multi-step logic, R3 is the sharper instrument.
According to LLMCheck benchmarks, GLM 5.2 and DeepSeek R3 are the two highest-scoring open-weights models in our database as of July 2026. The capability gap between them is smaller than the gap between either of them and the next open model down the list.
Architecture: 744B/40B vs 685B/37B MoE
Both models are sparse Mixture-of-Experts (MoE) designs. That means they hold an enormous pool of total parameters but route each token through only a small subset of "experts," keeping inference cost proportional to the active parameter count rather than the total. This is the architectural pattern that has let open labs reach frontier capability without frontier-scale inference bills.
| Architecture | GLM 5.2 | DeepSeek R3 |
|---|---|---|
| Developer | Zhipu AI | DeepSeek |
| Total Parameters | 744B | 685B |
| Active Parameters | 40B | 37B |
| Architecture | MoE | MoE |
| Design Bias | Agentic + balanced frontier | Deep reasoning + test-time compute |
| License | MIT | MIT |
The active-parameter counts are close — 40B for GLM 5.2 versus 37B for DeepSeek R3 — which is why their real-world inference costs land in the same ballpark despite GLM 5.2 carrying roughly 60B more total weights. The difference in totals reflects a difference in philosophy: GLM 5.2 spreads capability across a broader expert pool to handle a wider range of tasks, while DeepSeek R3 concentrates its training on extracting maximum reasoning depth from a slightly leaner activation path.
Benchmark Head-to-Head
Here is the full scorecard across the benchmarks that distinguish frontier reasoning models. Greens mark the leader on each row; near-ties are noted in the prose below.
| Benchmark | GLM 5.2 | DeepSeek R3 |
|---|---|---|
| SWE-Bench Pro (agentic coding) | 68.5% | 66% |
| SWE-Verified | 84% | — |
| HumanEval | 97% | — |
| AIME (competition math) | 94% | 95% |
| MATH | — | 92% |
| MMLU | 92% | — |
| GPQA (graduate science) | 89% | 88% |
| Arena AI ELO | 1565 | 1558 |
The pattern is clear once you read across the rows. GLM 5.2 leads on every coding and general-knowledge metric, and edges ahead on GPQA and Arena ELO. DeepSeek R3 takes the math-specific benchmarks — AIME by a single point and MATH outright. These are not blowouts in either direction; they are the fingerprints of two models tuned for slightly different jobs. A 7-point Arena ELO gap (1565 vs 1558) is within normal noise, meaning human raters find the two roughly interchangeable in open-ended chat.
Agentic Coding: The GLM 5.2 Edge
Coding is where the gap is most decision-relevant. SWE-Bench Pro measures whether a model can resolve real GitHub issues inside a working repository — reading the codebase, editing multiple files, running tests, and iterating until the patch passes. It is the closest public benchmark to "can this model do my job," and GLM 5.2's 68.5% leads R3's 66%.
GLM 5.2 widens that lead on SWE-Verified, the human-validated subset, where it scores 84%, and on HumanEval, where it reaches 97%. Just as important as the raw numbers is the behavior behind them: GLM 5.2 was explicitly tuned for agentic tool use — issuing shell commands, calling functions, managing multi-step plans — which is exactly the loop a coding agent runs. In practice, that translates to fewer derailed trajectories and cleaner recovery when a test fails on the first attempt.
For agentic coding pipelines — the kind that read a repo, write a patch, and run the suite — GLM 5.2 is the strongest open-weights option available in July 2026. According to LLMCheck benchmarks, it is the only open model to clear 68% on SWE-Bench Pro while also breaking 84% on SWE-Verified.
DeepSeek R3 is no slouch at code — 66% on SWE-Bench Pro would have been a frontier score a year ago — but its training prioritized reasoning chains over tool-driven iteration. If your coding workflow is heavy on autonomous, multi-turn agent loops, GLM 5.2 is the safer bet.
Pure Reasoning & Math: The DeepSeek R3 Edge
Flip the workload to pure reasoning and the ranking inverts. DeepSeek R3 was built around test-time compute scaling — the idea that a model gets measurably more accurate when you let it think longer, spending more tokens on its internal chain of thought before answering. R3 extracts more accuracy per additional reasoning token than GLM 5.2 does, which is why it pulls ahead on the hardest math.
Its 95% on AIME is the best of any open-weights model we track, and its 92% on MATH edges the field. For workloads built on formal logic — competition mathematics, theorem-style proofs, dense multi-step derivations, and problems where a single wrong step invalidates the whole answer — R3's deeper, more deliberate reasoning is the better tool. The advantage compounds when you raise the thinking budget: give R3 room to deliberate and its lead over GLM 5.2 on the hardest problems grows rather than shrinks.
That deliberation has a cost. Test-time compute scaling means R3 can spend a large number of tokens "thinking" before it answers, which raises latency and token spend on hard problems. For high-throughput or latency-sensitive applications, GLM 5.2's more economical reasoning may serve you better even on tasks R3 would ultimately win.
License: Both MIT, Both Free
This is the part that should make every developer pay attention. Both GLM 5.2 and DeepSeek R3 ship under the MIT license — one of the most permissive open-source licenses in existence. You can download the weights, fine-tune them on proprietary data, self-host them behind your firewall, and ship them inside a commercial product, all with no per-token fees and no usage caps.
On LLMCheck's scoring methodology, MIT earns the full 10/10 license-openness rating, the same top tier as Apache 2.0 and well above the gated terms attached to many "open" model families. There is no license-based tiebreaker here: both models are maximally open, so the decision comes down entirely to capability and the practical hardware question of what you can actually run.
Hardware Reality: Both Server-Class
Now the hard truth. Neither flagship is a Mac model. These are server-class systems that expect data-center memory.
| Deployment | GLM 5.2 | DeepSeek R3 |
|---|---|---|
| Memory at Q4 | ~390 GB | ~360 GB |
| Runs on any Mac? | No (full model) | No |
| Mac-runnable distill | GLM 5.2 Air (106B-A12B) | None official yet |
| Typical host | Multi-GPU server | Multi-GPU server |
At roughly 360–390GB of memory for a Q4 quantization, both full models are out of reach of even a maxed-out 128GB or 256GB Mac. Running these at home means a multi-GPU server, and running them well means serious VRAM. For the overwhelming majority of readers, the full flagships are an API or rented-GPU proposition, not a download-and-go one.
What to Actually Run on a Mac
Here is where the two camps diverge in practical terms. Zhipu AI shipped a distilled variant; DeepSeek, so far, has not.
GLM 5.2 Air is a 106B-A12B distillation of the flagship. With only ~12B active parameters and aggressive quantization, it fits on a 64GB Mac and generates around 30 tok/s — comfortably usable for interactive coding and reasoning. It is the escape hatch that lets you keep most of GLM 5.2's agentic personality on Apple Silicon without a server rack.
DeepSeek has no official Mac-runnable distill of R3 yet. That makes GLM 5.2 the only one of the two whose lineage you can actually run locally on a Mac today. If you want a second strong local option to pair with it, Qwen 4.1 32B-A3B is an excellent companion: a small-active MoE that runs fast on Apple Silicon and holds up well on both coding and reasoning, giving you a lighter-weight fallback when you do not need flagship-class output.
On Apple Silicon today, the realistic local stack is GLM 5.2 Air for frontier-flavored agentic work and Qwen 4.1 32B-A3B as a faster everyday companion. Reserve the full GLM 5.2 or DeepSeek R3 flagships for API calls or rented multi-GPU servers when a task genuinely needs frontier reasoning.
The Verdict
This is the rare comparison with no loser. GLM 5.2 and DeepSeek R3 are both genuine frontier open-weights models, both MIT-licensed, and both within a hair of each other on the aggregate. The decision is entirely about what you do with them.
- Pick GLM 5.2 if you write software, run coding agents, or want one balanced model that does almost everything well. Its SWE-Bench Pro and SWE-Verified leads are real, and the GLM 5.2 Air variant means you can run a piece of it on a Mac.
- Pick DeepSeek R3 if your work is math-heavy, proof-heavy, or built on deep multi-step reasoning, and you can afford the extra thinking tokens its test-time compute scaling demands.
- On a Mac specifically, GLM 5.2 wins by default — it is the only one of the two with an official local-runnable distillation. Pair GLM 5.2 Air with Qwen 4.1 32B-A3B for a complete Apple Silicon stack.
The deeper story is that in July 2026, the best models you can fully own are these two. The open frontier has caught the closed one, and it did so with permissive licenses attached. For builders who care about privacy, cost control, and not being locked into an API, that is the headline — and GLM 5.2 versus DeepSeek R3 is the new top of the open leaderboard.