GLM-5 vs GLM-4.7: what changed, what matters, and should you upgrade?

GLM-5, released February 11, 2026 by Zhipu AI (Z.ai), represents a large architectural leap from GLM-4.7: bigger MoE scale (≈744B vs ~355B total params), higher active parameter capacity, lower measured hallucination, and clear gains on agentic and coding benchmarks — at a cost in inference complexity and (sometimes) latency.

What is GLM-5 and why does its release matter?

What kind of model is GLM-5?

GLM-5 is the newest frontier open-weights large language model from Zhipu AI (Z.ai), released on February 11, 2026. It’s a Mixture-of-Experts (MoE) transformer that scales the GLM family up to ~744 billion total parameters, while activating roughly 40 billion parameters per inference (i.e., the model’s MoE routing keeps active compute much smaller than total parameter count). The model is offered with an MIT license and is optimized for agentic workloads — long-running, multi-step tasks such as orchestrating tools, writing and refining code, document engineering, and complex knowledge work.

What are the headline improvements vs earlier GLM variants?

Short list of the most consequential changes:

• Parameter scaling: GLM-5 ≈ 744B total (40B active) vs GLM-4.7’s ~355B total / 32B active — roughly a 2× jump in model scale.
• Benchmarks & factuality: Large uplift on independent benchmarks (Artificial Analysis Intelligence Index: GLM-5 = 50 vs GLM-4.7 = 42), and a large reduction in hallucination on the AA Omniscience metric (reported 56 percentage-point reduction relative to GLM-4.7).
• Agentic capability: Improved reliability for tool-calling, plan decomposition, and long-horizon execution (Z.ai positions GLM-5 for “agentic engineering”).
• Deployment & chips: Built and benchmarked to run on domestic Chinese inference hardware (Huawei Ascend and others), reflecting Z.ai’s move toward varied chip stacks.

Why it matters: GLM-5 narrows the gap between open-weights and proprietary frontier models on agentic and knowledge tasks — making high-capability, open-source models a realistic option for enterprises that need controllable deployments and licensing flexibility.

What’s new in GLM-5 (detailed)

Positioning: “Agentic engineering” at scale

GLM-5 is explicitly positioned by Z.ai as a model for “agentic engineering”: a class of use cases where the model plans, issues tool calls, inspects results, and iterates autonomously across many steps (e.g., build a CI pipeline, triage and fix failing test suites, or stitch together microservices). This is a strategic shift from purely single-turn code generation to models designed to run and reason across execution traces and tool outputs.

Thinking modes, preserved/interleaved reasoning

GLM-5 introduces refined “thinking” modes (sometimes branded in docs as interleaved thinking, preserved thinking), meaning the model can emit — and then reuse — internal reasoning traces in subsequent turns and tool calls. Practically, this reduces re-derivation costs in long workflows and improves consistency when an agent must maintain plan state across tool results. GLM-4.7 introduced earlier thinking variants and tool-aware behavior; GLM-5 refines the mechanics and training recipes to make those traces more reliable and reusable.

Long-context engineering and system stability

GLM-5 training and fine-tuning explicitly test generation with very long contexts (202,752 tokens during SFT/evaluation runs). That’s a practical increase that matters once you need the model to see multiple repositories, test logs, and orchestration outputs in one prompt. Evaluation setups that push generation lengths to 131,072 tokens for some reasoning workloads. This is a notable engineering effort to mitigate the usual instability when conditioning on huge contexts.

Architecture and scaling (MoE)

Public reports indicate GLM-5 uses a large MoE (mixture-of-experts) architecture with several hundred billion parameters in total (public tallies list ~744–745B). GLM-4.7 has MoE and Flash variants tuned for different deployment tradeoffs (for example, “Flash” variants with smaller active parameter counts for local or low-cost inference). The MoE design helps GLM-5 push peak capability while enabling configuration choices (lower active parameter counts for cheaper inference). Expect different inference profiles (latency, VRAM) depending on which variant you deploy.

How did Z.ai scale and train GLM-5 compared with GLM-4.7?

Core architectural differences

Takeaway: GLM-5 nearly doubles total capacity compared to GLM-4.7 and increases active parameters, which contributes to better reasoning and synthesis abilities, especially for long-form technical content, extended reasoning pipelines, and complex code engineering tasks.

Architecture: what changed?

GLM-4.7 is a mixture-of-experts (MoE) design in its larger variants (documented as ~355B total parameters with a smaller active-set per token). GLM-5 retains MoE-style sparsity ideas but layers in a new sparse attention mechanism — the report calls it DeepSeek Sparse Attention (DSA) — that dynamically allocates attention resources to tokens it deems important. The claim is that DSA reduces inference/training cost while preserving (or improving) the model’s long-context reasoning, allowing the model to handle contexts far longer than legacy checkpoints while keeping compute manageable.

Scale: parameters and data

• GLM-4.7: documented as approximately 355 billion total parameters for the main MoE version (with a much smaller active parameter set per forward pass for efficiency).
• GLM-5: reported at ~744 billion parameters and trained with ~28.5 trillion tokens in its pretraining budget, with a training emphasis on code and agentic sequences. That combination is intended to improve code synthesis and sustained agentic planning.

The parameter jump, alongside token-budget expansion and architectural updates, is the primary input-side reason GLM-5 shows better numerical results on code and agentic leaderboards.

Training strategy and post-training (RL)

Where GLM-4.7 introduced “interleaved” or retained thinking modes to improve multi-step reasoning and tool usage, GLM-5 formalizes that pipeline by:

1. Expanding the context length via a mid-training schedule (the team reports progressive context extension up to 200K tokens).
2. Implementing a sequential RL post-training pipeline (Reasoning RL → Agentic RL → General RL) together with on-policy cross-stage distillation to avoid catastrophic forgetting.
3. Adding asynchronous RL and decoupled rollout engines to scale agent trajectories during RL without synchronization bottlenecks.

These methods are specifically aimed at improving long-horizon agentic behavior — for example, keeping stable internal state over long sessions where the model performs multiple dependent tool calls and code edits.

How do GLM-5 and GLM-4.7 compare in performance and capability?

Benchmarks & Intelligence Measures

Interpretation:

• GLM-5 outperforms GLM-4.7 broadly on core coding and reasoning benchmarks by measurable margins. This is especially clear in multi-step automation, problem decomposition, and deep logic tasks.
• Improvements are non-trivial: e.g., Terminal Bench capability jumps from ~41% to 56%, a major relative gain in agentic automation reliability.
• On reasoning tests (like internal HLE metrics), GLM-5 shows stronger raw and tool-enhanced reasoning outputs.
• Shows measurable gains on real-world agentic tests: in the CC-Bench-V2 frontend HTML ISR metric GLM-5 recorded 38.9% vs GLM-4.7’s 35.4% on a subset of frontend tasks. (This is one of the automatically-evaluated metrics used to show practical front-end development competence.)

Context Size & Long-Form Tasks

• Both models support large contexts (~200k tokens) — meaning they can consume and reason over longer documents, codebases, or dialogues.
• Real-world anecdotal reporting suggests GLM-5 deployments have occasionally shown perceived context management issues on some platforms — but this may reflect host-specific limits rather than the model design itself.

Tool and Function Calling

Both support structured function/tool invocation; GLM-5 simply executes more complex script logic with greater fidelity, especially across extended branches of operations.

Examples: How tasks differ in output quality

Coding Example (conceptual)

• GLM-4.7: Produces competent single-file scripts with correct syntax and readable logic.
• GLM-5: Excels at multi-file code generation, deep debugging suggestions, and long feedback loops with minimal context truncation.

Reasoning & Planning

• GLM-4.7: Good multi-step reasoning but occasionally stalls on very deep reasoning chains.
• GLM-5: Better at chunking reasoning, recalling earlier steps, and navigating long chains — helpful for data synthesis and multi-domain strategies.

How do latency and cost change if we move from GLM-4.7 to GLM-5?

Latency tradeoffs and where GLM-4.7 still wins

Short messages & snappy UIs: Benchmarks from practitioners show GLM-5 can add a small fixed overhead on short responses (routing and expert-selection bookkeeping) that can manifest as slightly higher latency for tiny payloads. For ultra-low latency small-message UIs, GLM-4.7 or Flash variants remain attractive.

GLM-5 compared with GLM-4.7:

• GLM-4.7: input $0.60/1M tokens, output $2.20/1M tokens.
• GLM-5: input $1.00/1M tokens, output $3.20/1M tokens.

Cost vs. human editing tradeoff

A higher model price can be justified when GLM-5 meaningfully reduces downstream human time (e.g., editing merge requests, triaging automated fixes, or avoiding repeated model calls). A simple decision rule:

If GLM-5 reduces manual editing time by > X% (X depends on human labor rate and number of tokens per workflow), it can be cost-effective despite higher per-token cost. Several blog analyses modeled such break-even conditions and found GLM-5 often pays off for heavy, repetitive agentic workflows (e.g., automated code repair at scale).

Latency & hardware

Inference VRAM & latency depend on variant (Flash, FlashX, full MoE). Community guides show that GLM-4.7 FlashX and 30B Flash variants are deployable on 24GB GPUs; full MoE variants require large multi-GPU setups. GLM-5’s full configurations will expect materially higher resource needs for the same throughput, though MoE sparsity helps reduce active compute per token. Expect engineering investment to tune quantization, memory-mapping, and streaming for production.

When should you upgrade from GLM-4.7 to GLM-5?

Upgrade if:

• You need better multi-file code reasoning, long-context agent orchestration, or higher end-to-end agent success rates.
• Your tasks are high-value and justify higher per-request infra complexity and cost.

Stay with GLM-4.7 if:

• Your workload is high-volume, short prompts (classification, tagging), where cost & latency predictability matter more than marginal quality gains.
• Use cases that favor staying with GLM-4.7
• High-throughput, short payloads: Chatbots, autosuggest, tiny paraphrasing jobs — GLM-4.7 (especially Flash variants) will often be cheaper and lower latency.
• Constrained budgets and volume tasks: For tagging, classification, or micro-tasks executed at scale, GLM-4.7’s efficiency and lower per-token price are compelling.
• You lack the infra or budget to handle MoE sharding / complex autoscaling.

How do I choose the model in my API calls? (examples)

cURL — switch model ID (CometAPI / OpenAI-compatible example):

# GLM-4.7
curl -X POST "https://api.cometapi.com/v1/chat/completions" \
 -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
 -d '{"model":"glm-4.7","messages":[{"role":"user","content":"Summarize this repo..."}],"max_tokens":800}'
# GLM-5
curl -X POST "https://api.cometapi.com/v1/chat/completions" \
 -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
 -d '{"model":"glm-5","messages":[{"role":"user","content":"Summarize this repo..."}],"max_tokens":1200}'

Python (requests): change the model field to route to GLM-4.7 or GLM-5 — the rest of the client code can stay the same.

Final assessment:

GLM-5 reads as evolutionary with important inflection points:

• Evolutionary because it carries forward the GLM family’s MoE and reasoning-first design and continues the iterative improvement pattern (4.5 → 4.6 → 4.7 → 5).
• Inflection because it materially increases scale, introduces DSA, and commits to an RL curriculum specifically tailored to long-horizon agentic tasks — all of which produce meaningful, measurable improvements across a range of practical benchmarks.

If you evaluate by leaderboard placement alone, GLM-5 claims open-weights leadership on several metrics and narrows gaps with top proprietary systems in agentic and coding tasks. If you evaluate by developer experience and latency-sensitive usage, practical pros and cons remain to be demonstrated in larger deployments and over time. That means GLM-5 is compelling where the use case demands sustained agentic competence; GLM-4.7 remains a mature, faster, and more cost-savvy choice for many current production needs.

Developers can access GLM-5 and GLM-4.7 via CometAPI now.To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

Ready to Go?→ Sign up fo GLM-5 today !

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