GLM 5.2 vs DeepSeek V4 Pro: Full 2026 Comparison

GLM 5.2 and DeepSeek V4 Pro are the two open-weight models that matter most for coding work in mid-2026. Both ship under MIT licenses, both support 1M-token context windows, and both claim benchmark scores that rival proprietary models from OpenAI and Anthropic. But they are built for different workloads, priced at different tiers, and architected with different tradeoffs.

This comparison breaks down every verifiable difference between GLM 5.2 and DeepSeek V4 Pro so you can choose the right model for your coding agents, software engineering pipelines, or production applications.

What is GLM 5.2?

GLM 5.2 is Z.ai's (formerly Zhipu AI) flagship open-weight model for long-horizon tasks, released on June 13, 2026. It uses a mixture-of-experts architecture with approximately 744 billion total parameters and 40 billion active per token (per the GLM-5 Technical Report), paired with a 1M-token context window and a 128K-token output ceiling. Z.ai positions the model for sustained, repo-scale software engineering where the agent must maintain quality across long, complex coding-agent trajectories.

Z.ai, a Beijing-based company spun out of Tsinghua University, completed a Hong Kong Stock Exchange IPO in January 2026. The company has shipped four flagship-tier coding releases in about four months (GLM-5, GLM-5-Turbo, GLM-5.1, and GLM-5.2), with each version leaning harder into multi-file, multi-step engineering workflows.

GLM 5.2 launched first through the GLM Coding Plan subscription and arrived on the standalone API on June 16, 2026 at $1.40 per million input tokens and $4.40 per million output tokens, per Z.ai's developer docs. Its MIT-licensed open weights became available on Hugging Face shortly after.

Key characteristics:

• Two thinking-effort levels: High and Max (no lightweight mode)
• Drop-in compatibility with ZCode, Claude Code, OpenCode, and other agentic coding tools via Anthropic-compatible endpoint
• IndexShare sparse attention mechanism for efficient long-context processing
• Multi-token prediction layer for faster decoding

What is DeepSeek V4 Pro?

DeepSeek V4 Pro is DeepSeek's reasoning and coding flagship, released on April 24, 2026, the same day OpenAI shipped GPT-5.5. It runs a 1.6-trillion-parameter mixture-of-experts architecture with 49 billion parameters active per token, and it supports a 1M-token context window with up to 384K tokens of output per request, per DeepSeek's model card.

The V4 series introduced three key architectural upgrades over its predecessor: a hybrid attention mechanism (Compressed Sparse Attention + Heavily Compressed Attention) that cuts inference FLOPs to 27% and KV cache to 10% of V3.2 at 1M context, Manifold-Constrained Hyper-Connections for training stability, and the Muon optimizer for faster convergence. The model weights use FP4 precision for MoE expert layers and FP8 for most other parameters. DeepSeek pre-trained V4 on over 32 trillion tokens, more than double V3's training data.

The model launched at $1.74/$3.48 per million tokens, but a 75% promotional discount brought that to $0.435/$0.87. As of May 31, 2026, the discounted price became permanent, per DeepSeek's pricing page.

Key characteristics:

• Three thinking modes: Non-think (fast), Think High (analytical), Think Max (full reasoning)
• Hybrid attention architecture enabling practical 1M-token context at production scale
• 384K maximum output tokens, the highest in this comparison
• Text-only at launch; vision (image input) was added post-launch and is now available via API and chat

Head-to-head comparison

The table below captures the confirmed specifications for both models as of July 2026.

GLM 5.2 vs DeepSeek V4 Pro specification comparison. Pricing as of July 2026. Verify current rates on each vendor's pricing page before committing.

Coding and software engineering benchmarks

GLM 5.2 outperforms DeepSeek V4 Pro across every shared software engineering benchmark in Z.ai's official table, often by wide margins. DeepSeek V4 Pro leads on competitive programming and algorithmic coding tasks where GLM 5.2 has not published scores.

The most dramatic gaps appear on long-horizon coding benchmarks. On FrontierSWE, which measures multi-hour open-ended engineering projects, GLM 5.2 scores 74.4 compared to DeepSeek V4 Pro's 29.0, a 45.4-point separation. On DeepSWE, GLM scores 46.2 versus DeepSeek's 8.0. These benchmarks specifically test the sustained, repo-scale work that GLM 5.2's 1M context window was designed for, and the gaps are the widest across any shared benchmark category.

On the more commonly cited evaluations, GLM 5.2 leads SWE-bench Pro by 6.7 points (62.1% vs 55.4%) and Terminal-Bench 2.1 (Terminus-2 harness) by 17 points (81.0 vs 64.0). On SWE-bench Verified, DeepSeek V4 Pro scores 80.6%, but GLM 5.2 has not published a score on this specific benchmark version.

For competitive programming, DeepSeek V4 Pro owns the leaderboard. Its 93.5% on LiveCodeBench ranks first globally across all models (open and closed), and its 3206 Codeforces rating exceeds what most human competitive programmers achieve. GLM 5.2 has not published scores on these algorithmic benchmarks.

Coding benchmark comparison. GLM 5.2 scores from Z.ai's official model card and blog. DeepSeek V4 Pro scores from Z.ai's cross-model table, DeepSeek's model card, and DeepSeek's published benchmarks. FrontierSWE evaluation conducted by Proximal as of June 16, 2026. Terminal-Bench 2.1 scores use the Terminus-2 harness (Z.ai also reports 82.7 using Claude Code as the "best reported harness"). "Not published" means neither vendor has released a score on that specific benchmark.

The pattern is clear. GLM 5.2 dominates every shared software engineering benchmark, and the advantage grows on longer-horizon tasks. FrontierSWE and DeepSWE, which require sustained multi-hour engineering effort, show the widest gaps. DeepSeek V4 Pro excels at the kind of work competitive programmers do: algorithmic problem-solving, mathematical reasoning under constraints, and single-pass code generation. One early independent evaluation from Semgrep found GLM 5.2 competitive with Claude Opus 4.8 on cybersecurity coding benchmarks, lending some outside validation to Z.ai's numbers.

If your workload involves agents iterating over a codebase for hours to ship a feature, GLM 5.2's benchmark profile is the stronger fit by a wide margin. If your workload involves generating discrete solutions to well-defined problems, DeepSeek V4 Pro has stronger receipts.

Reasoning, math, and agentic benchmarks

The official Z.ai benchmark table includes DeepSeek V4 Pro scores across reasoning, math, and agentic evaluations. The results show a more competitive picture than the coding section.

Reasoning, math, and agentic benchmark comparison. All scores sourced from Z.ai's official cross-model comparison table in the GLM 5.2 model card. GPQA Diamond tests graduate-level scientific reasoning. HLE is an adversarial evaluation designed to resist model optimization. Tool-Decathlon and MCP-Atlas measure agentic tool-use capabilities.

The picture is more mixed than the coding section. GLM 5.2 leads on five of seven benchmarks, including AIME 2026 (99.2% vs 94.6%), HLE with tools (54.7% vs 48.2%), and GPQA Diamond (91.2% vs 90.1%). But DeepSeek V4 Pro takes HMMT Feb. 2026 math (95.2% vs 92.5%) and Tool-Decathlon (52.8 vs 48.2), an agentic benchmark that measures multi-tool orchestration. The Tool-Decathlon result is notable because it suggests DeepSeek V4 Pro's agentic capabilities are not uniformly weaker than GLM 5.2's, despite GLM leading on MCP-Atlas and coding-agent benchmarks.

NIST's Center for AI Standards and Innovation (CAISI) evaluated DeepSeek V4 Pro in April 2026 and concluded that its capabilities lag roughly eight months behind the U.S. frontier. The evaluation also noted that DeepSeek V4 Pro's actual performance fell below its self-reported benchmarks on CAISI's non-public evaluations. GLM 5.2 has not been subject to a comparable independent government evaluation.

Context window and output limits

Both models advertise 1M-token context windows, but the practical details differ in ways that matter for production deployments.

GLM 5.2's 1M context is the result of a 5x increase over GLM 5.1's 200K window. Z.ai specifically calls it a "usable" 1M, positioning it against models that technically accept large inputs but lose coherence in the middle. Maximum output per response sits at 128K tokens (131,072 exactly), which is enough for large multi-file diffs but significantly less than DeepSeek V4 Pro's ceiling.

DeepSeek V4 Pro supports up to 384K tokens of output per request. For coding agents that need to produce entire modules, comprehensive test suites, or large-scale refactoring diffs in a single pass, that 3x output advantage matters. DeepSeek also recommends setting context to at least 384K tokens when using Think Max mode, which suggests the reasoning chain itself consumes substantial output budget at maximum effort.

For most real-world coding tasks (fixing bugs, adding features, writing tests for existing code), the output difference rarely surfaces. A typical bug fix might touch two to five files with a total diff under 5,000 tokens. The gap becomes relevant when you need the model to generate an entire new service or write comprehensive documentation in one shot.

Pricing comparison

DeepSeek V4 Pro is roughly 5x cheaper per output token than GLM 5.2 at first-party API rates. For cost-sensitive production workloads, the gap is decisive.

API pricing comparison as of July 2026. GLM 5.2 pricing from Z.ai's standalone API (live since June 16, 2026). DeepSeek V4 Pro pricing reflects the permanent 75% discount effective since May 31, 2026. Verify current rates on each vendor's pricing page.

GLM 5.2 offers an alternative pricing path through the GLM Coding Plan subscription, which charges a flat monthly fee with prompt-based (not token-based) limits. Reported rates start around $10 to $18/month for the Lite tier (with promotional pricing that steps up after the introductory period). For developers who live inside a coding IDE all day, the subscription can be cheaper than metered tokens.

DeepSeek V4 Pro's cache-hit pricing deserves special attention. At $0.003625 per million cached input tokens, repeated-context workloads (common in agentic coding where the same codebase context loads repeatedly) can run at nearly zero input cost. A workload that hits 90% cache rates pays an effective blended rate well under $0.50 per million tokens.

Both models are also available through third-party providers like OpenRouter and DeepInfra, often at slightly different rates. GLM 5.2's open weights can be self-hosted at no per-token cost, though the 744B MoE model requires substantial GPU infrastructure.

Architecture and efficiency

Both models use mixture-of-experts architectures, but they approach the design differently.

GLM 5.2 activates approximately 40 billion parameters per token from a 744-billion-parameter total. Its IndexShare sparse attention mechanism optimizes memory and compute at long context lengths, and its multi-token prediction layer accelerates decoding. The model's two thinking modes (High and Max) eliminate any lightweight option, signaling that Z.ai built this model for serious, extended engineering sessions rather than quick lookups.

DeepSeek V4 Pro activates 49 billion parameters per token from a 1.6-trillion-parameter total. The hybrid attention mechanism (CSA + HCA) is its architectural highlight: at 1M-token context, it requires only 27% of the single-token inference FLOPs and 10% of the KV cache memory that DeepSeek V3.2 needed for the same context length. That efficiency gain makes 1M-token inference economically viable in production, not just a specification on paper.

DeepSeek V4 Pro also includes a Non-think mode alongside Think High and Think Max. This matters for routing: you can send simple tasks (code formatting, linting, basic completion) through Non-think at lower latency and cost, and reserve Think Max for complex multi-step reasoning. GLM 5.2 requires deliberate reasoning on every request.

Self-hosting and deployment

Both models publish MIT-licensed open weights, giving teams full control over infrastructure, fine-tuning, and data privacy. The practical self-hosting requirements differ substantially.

GLM 5.2 at ~744B total parameters requires significant GPU memory for the full-precision model. Z.ai published FP8 variants on Hugging Face, which reduce the memory footprint but still demand multi-GPU configurations. For organizations with data sovereignty requirements or compliance constraints that prohibit sending code to external APIs, the MIT license removes vendor lock-in entirely.

DeepSeek V4 Pro at 1.6 trillion total parameters is the largest open-weight model currently available. Self-hosting the full model requires even more GPU infrastructure than GLM 5.2. The model natively uses FP4 precision for MoE expert weights and FP8 for most other parameters, which reduces the memory footprint compared to full-precision alternatives. Cloud providers like DeepInfra and NVIDIA also offer hosted inference endpoints as an intermediate option between full self-hosting and the DeepSeek API.

For most teams, the self-hosting decision comes down to volume and compliance. At high enough request volumes (hundreds of thousands of daily API calls), the fixed cost of GPU infrastructure can beat per-token API pricing. For data-sensitive workloads in regulated industries, self-hosting open-weight models eliminates third-party data exposure.

Which model should you choose?

The right choice depends on your workload, not the headline benchmark number.

Choose GLM 5.2 if:

• Your primary use case is agentic, multi-step software engineering (feature development, repo-scale refactoring, long debugging sessions).
• You already use Claude Code, OpenCode, or similar agentic tools and want a drop-in model swap via the Anthropic-compatible endpoint.
• You prefer subscription-based pricing through the GLM Coding Plan over metered token billing.
• You value the highest available open-weight scores on long-horizon coding benchmarks (FrontierSWE, Terminal-Bench, SWE-bench Pro).

Choose DeepSeek V4 Pro if:

• Cost is a primary constraint, and you need frontier-class performance at roughly one-fifth of GLM 5.2's output token price.
• Your workload includes competitive-programming-style tasks, algorithmic problem-solving, or math-heavy reasoning alongside coding.
• You need the flexibility of a Non-think mode for lightweight tasks to avoid overspending on simple requests.
• You need maximum output length (384K tokens) for generating large codebases or documentation in a single pass.
• You want the broadest independently verified benchmark coverage to reduce risk.

For most production coding workloads, DeepSeek V4 Pro's cost advantage is hard to ignore. The pricing gap compounds quickly at scale. But for teams where agentic coding quality on real repository tasks is the bottleneck, GLM 5.2's dominance across long-horizon benchmarks (FrontierSWE, DeepSWE, Terminal-Bench, SWE-bench Pro) points to a meaningfully stronger model for sustained engineering work.

A practical approach: run both models on your actual codebase and measure the acceptance rate of generated patches. The model that produces more usable code at lower total cost (including human review time) is the right one for your team, regardless of benchmark rankings.

Skip the model comparison entirely

Evaluating open-weight models, configuring API endpoints, and managing token budgets makes sense if you have a development team that works with LLMs directly. But if your goal is to ship working software rather than optimize a coding agent, the model comparison becomes a detour.

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If you came to this comparison looking for the best model to power a coding project, both GLM 5.2 and DeepSeek V4 Pro are strong picks. But if you want to skip the model comparison and API setup altogether, Start Building on Emergent and let the platform handle the engineering.