If you are choosing between two Chinese open-weight model families for a production workload in 2026, the deepseek vs glm decision usually comes down to three numbers: price per million tokens, SWE-Bench Verified score, and how much agentic work you actually need the model to do. Both labs ship MIT-licensed weights. Both undercut Claude and GPT-5 on cost. But they make different trade-offs — DeepSeek V4 leans on extreme efficiency and a 1M-token context window, while Zhipu’s GLM-5 family pushes harder on agentic engineering and Claude-style coding behaviour. This guide gives you the verdict up front, the head-to-head detail underneath, and a worked cost example you can plug into your own workload.

Verdict: who wins the deepseek vs glm matchup

For the majority of teams, DeepSeek V4-Flash is the better default. It is cheaper than every paid GLM tier, ships a 1M-token context window, and the legacy deepseek-chat / deepseek-reasoner IDs route to it until they retire on July 24, 2026 at 15:59 UTC. Pick GLM-5 instead if you need a Claude-style agentic coding partner with strong long-horizon tool use and you can absorb the price premium — it scores 77.8% on SWE-bench Verified, 92.7% on AIME 2026, and 86.0% on GPQA-Diamond, and Z.ai pitches it explicitly as a Claude Opus alternative.

Pick DeepSeek V4-Pro only when you need frontier-tier reasoning at lower cost than GLM-5 on output tokens but with a much larger context window. Avoid GLM-4.5 / 4.6 for new builds — they are previous-generation. Avoid the legacy DeepSeek model IDs beyond the retirement date.

At-a-glance comparison table

Sources: DeepSeek V4 announcement (April 24, 2026); GLM-4.7 scores 73.8% on SWE-bench and 85.2% on HumanEval; GLM-5 API priced at $1.00 per million input tokens and $3.20 per million output tokens; GLM-4 family ranges from free Flash variants to $2.20 per million output tokens for GLM-4.7.

Coding: the GLM home turf, narrowly

Coding is where this comparison gets interesting. Z.ai has invested heavily in agentic engineering — GLM-5 is designed for complex system engineering and long-range Agent tasks, demonstrating strong deep-reasoning performance in backend architecture, complex algorithms, and stubborn bug fixing, and integrates DeepSeek Sparse Attention for higher token efficiency while preserving long-context quality. The borrowing of DeepSeek’s own attention mechanism is telling: it confirms DeepSeek’s architectural lead even as GLM closes the application gap.

For DeepSeek, the V4-Pro number to know is its SWE-Bench Verified score of 80.6% from the April 24, 2026 announcement, which edges out GLM-5’s 77.8%. But raw scores are only half the story. In practice:

• Greenfield code generation — both are excellent. V4-Flash at $0.28 per million output tokens is roughly 8× cheaper than GLM-5 on output, which matters when an agent loop emits hundreds of thousands of tokens.
• Long-horizon agent runs — GLM-5.1 can work independently for up to 8 hours in a single run, enabling a full loop from planning and execution to iterative refinement and final delivery, with comprehensive capability alignment with Claude Opus 4.6. This is GLM’s strongest pitch.
• Massive codebases — DeepSeek V4’s 1M-token default context dwarfs GLM-5’s 200K. If you need to feed an entire monorepo into a single prompt, DeepSeek wins by default.

For day-to-day coding tasks, our DeepSeek for coding guide walks through prompt patterns that work well on V4-Flash, and our DeepSeek Coder vs Copilot piece covers the IDE-integration angle separately.

Reasoning: V4-Pro thinking mode vs GLM-5

Both families ship dedicated reasoning behaviour. On DeepSeek V4, thinking mode is a request parameter — not a separate model ID. You set reasoning_effort="high" with extra_body={"thinking": {"type": "enabled"}} on either V4-Pro or V4-Flash and the API returns reasoning_content alongside the final content. For maximum effort, use reasoning_effort="max", which requires max_model_len >= 393216 (384K tokens) to avoid truncation.

GLM-5 takes a different approach: separate model variants (GLM-5, GLM-5-Turbo, GLM-5.1) for different latency/quality trade-offs. Z.ai released GLM-5.1 on March 27, 2026, claiming a coding evaluation score of 45.3 compared to Claude Opus 4.6’s 47.9, a 28% improvement over the GLM-5 baseline score of 35.4. Worth noting: these benchmarks are self-reported by Z.ai and have not yet been independently verified.

On math and science, GLM-5 scores 92.7% on AIME 2026 and 86.0% on GPQA-Diamond. Cross-check the DeepSeek V4 technical report for V4-Pro’s matching numbers before you commit to either for a math-heavy workload — DeepSeek for math covers practical setup.

Writing and general chat

For non-coding chat — translation, drafting, summarisation, customer-facing copy — V4-Flash is the obvious pick on cost alone. The GLM family carries a quirk worth flagging: in December 2025, MIT researchers documented that GLM-series models identified themselves as Claude roughly 50% of the time when queried through non-standard methods. That doesn’t make GLM unusable, but if your product cares about model identity disclosure, it’s a real consideration.

Both providers ship in English and Chinese first; for everything else, our DeepSeek languages reference covers the supported set.

Pricing: the headline gap

Here are the rates you should plug into a spreadsheet today, all per 1M tokens, USD:

Zhipu charges US$1.40 per million input tokens and about US$4.40 per million output tokens for GLM-5.1, while Anthropic’s Claude Opus 4.6 cost US$5 per million input tokens and US$25 per million output tokens as of February 2026. So GLM is cheaper than Western frontier models — but DeepSeek V4-Flash undercuts every paid GLM tier on both input and output. For an end-to-end pricing tour, see DeepSeek API pricing.

Two important caveats:

1. Off-peak discounts on the DeepSeek API ended on September 5, 2025 and were not reintroduced with V4. Don’t budget on the assumption of a night-time discount.
2. Z.ai removed its $3/month promotional pricing on February 11, 2026, so if you’ve seen that number floating around it no longer applies — current pricing starts at ~$10/month.

Worked cost example: 1M API calls a month

Concrete math beats hand-waving. Take a typical workload: 1,000,000 API calls a month, with a 2,000-token system prompt (cached across calls), a 200-token user message (uncached on each call), and a 300-token response. We’ll cost it on deepseek-v4-flash, then on GLM-5 for comparison.

DeepSeek V4-Flash

Cached input    : 2,000 × 1,000,000 = 2.0B tokens × $0.028/M  = $56.00
Uncached input  :   200 × 1,000,000 = 200M tokens × $0.14/M   = $28.00
Output          :   300 × 1,000,000 = 300M tokens × $0.28/M   = $84.00
                                                              -------
Total                                                         $168.00

GLM-5 (no published cache-hit tier)

Input           : 2,200 × 1,000,000 = 2.2B tokens × $1.00/M   = $2,200.00
Output          :   300 × 1,000,000 = 300M tokens × $3.20/M   =   $960.00
                                                              ---------
Total                                                         $3,160.00

Same workload, ~19× cost difference — driven mostly by DeepSeek’s context-caching tier, which charges $0.028 per million tokens on repeated prefixes. DeepSeek context caching explains how the cache-hit detection works in practice. If you’d rather model your own numbers, the DeepSeek pricing calculator covers V4-Flash and V4-Pro.

Note that running the same example on V4-Pro at $0.145 / $1.74 / $3.48 lands at $1,682.00 — still cheaper than GLM-5 by roughly half, but the gap narrows sharply at the frontier tier.

API ergonomics

Both providers expose OpenAI-compatible endpoints. On DeepSeek, chat requests hit POST /chat/completions, the OpenAI-compatible endpoint at https://api.deepseek.com. DeepSeek also ships an Anthropic-compatible surface against the same base URL, so the Anthropic SDK works by swapping base_url and api_key. Minimal Python:

from openai import OpenAI
client = OpenAI(base_url="https://api.deepseek.com", api_key="...")
resp = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Summarise this PR."}],
    temperature=0.0,
    max_tokens=2000,
)

The DeepSeek API is stateless — you must resend the conversation history with each request. Web and app surfaces maintain session history; the API does not. Useful parameters worth knowing: temperature (0.0 for code, 1.3 for general chat, 1.5 for creative writing), top_p, max_tokens, reasoning_effort, JSON mode, tool calling, streaming, context caching, FIM completion (Beta, non-thinking only), and Chat Prefix Completion (Beta).

JSON mode is designed to return valid JSON, not guaranteed. The model may occasionally return empty content; include the word “json” plus a small example schema in your prompt and set max_tokens high enough to avoid truncation. See DeepSeek API JSON mode for the full handling pattern.

GLM ships its own quirks: configure your coding tool to point at the GLM Coding endpoint at https://api.z.ai/api/coding/paas/v4 and select the compatibility mode — GLM supports both OpenAI and Anthropic API formats. Registration works with international email, but billing prioritizes AliPay and WeChat Pay, rate limits on new accounts are conservative, and support documentation is primarily in Chinese, though English docs exist for the core API endpoints. For non-Chinese teams, this is real friction.

If you maintain integrations against the legacy deepseek-chat or deepseek-reasoner IDs, both currently route to deepseek-v4-flash and will be retired on July 24, 2026 at 15:59 UTC. Migration is a one-line model= swap; base_url does not change. DeepSeek OpenAI SDK compatibility covers the swap in detail.

Privacy, ecosystem and licensing

Both labs publish open weights under MIT for current-generation releases — V4-Pro, V4-Flash, V3.2, R1 on the DeepSeek side; GLM-5, GLM-4.7 and the Flash variants on the GLM side. GLM-5 is released under the MIT license on Hugging Face, available via the Z.ai API, and on OpenRouter.

Hardware story: Zhipu AI trained the full GLM-5 family exclusively on Huawei Ascend 910B accelerators, making it one of the most prominent demonstrations that competitive frontier models can be built without access to Nvidia GPUs, particularly significant given Zhipu’s placement on the US Entity List since January 2025. DeepSeek’s training infrastructure is well-documented in its V3 and V4 technical reports. Both labs process API requests on servers subject to Chinese law; if that is a hard constraint for your industry, self-hosting open weights is the answer for either family.

When to pick which

• Pick DeepSeek V4-Flash for high-volume chat, RAG, classification, and general-purpose coding where price-per-task dominates.
• Pick DeepSeek V4-Pro for frontier-tier coding agents and reasoning workloads where you need 1M-token context and the SWE-Bench Verified lift over Flash.
• Pick GLM-5 / GLM-5.1 when you specifically want a Claude-style long-horizon agentic engineering partner and you’ve validated that GLM’s tool-calling stack fits your framework.
• Pick GLM-4.7-Flash only as a free fallback for non-critical sub-tasks; it is genuinely free but capability-limited.
• Self-host instead if your data residency rules prohibit either Chinese cloud — both families publish open weights under MIT.

For a wider field of view, see DeepSeek vs Qwen and DeepSeek vs Kimi, or browse the full AI comparison hub.

Bottom line

DeepSeek V4-Flash is the value pick: cheaper than every paid GLM tier, with a 1M-token context window and the SWE-Bench Verified numbers from V4-Pro to fall back on when you need frontier-tier behaviour. GLM-5 is the better choice if you specifically want Claude-style agentic engineering, are willing to absorb roughly 19× the per-call cost of V4-Flash on the worked example above, and don’t need the 1M-token window. For most teams reading this, the deepseek vs glm decision ends with model="deepseek-v4-flash" and a calendar reminder to migrate off legacy IDs before July 24, 2026.

Last verified: 2026-04-25. DeepSeek AI Guide is an independent resource and is not affiliated with DeepSeek or its parent company. Model IDs, pricing and API behaviour change; check the official DeepSeek documentation and pricing page before committing to a production decision.