Real Test: DeepSeek-V4 vs GLM-5.1 vs GPT-5.5 — The Results Are Surprising!

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April 2026 shook the AI world: OpenAI and DeepSeek dropped their flagship models on the same day. Right behind them, Zhipu’s GLM-5.1 entered the scene. Three top-tier models, one showdown. We ran the benchmarks — here’s what actually matters.

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DeepSeek-V4 vs GLM-5.1 vs GPT-5.5 comparison cover

1. Quick Overview of All Three Models

Before diving deep, here’s the specs at a glance:

ModelDeveloperRelease DateContext LengthOpen Source
DeepSeek-V4-ProDeepSeekApril 24, 20261M tokensMIT License
DeepSeek-V4-FlashDeepSeekApril 24, 20261M tokensMIT License
GLM-5.1Zhipu AIApril 2026128K tokensPartially open
GPT-5.5OpenAIApril 23, 2026400K-1M tokensClosed source

TL;DR:

  • DeepSeek-V4: Open-source long context, flexible deployment, friendly pricing
  • GLM-5.1: Coding Agent focus, strong Chinese language understanding
  • GPT-5.5: Peak performance, mature tooling, premium price tag

2. Hands-On Comparison: Where Each Model Excels

2.1 Coding Ability

Coding is where these models really duke it out. Check the benchmark numbers:

BenchmarkGPT-5.5DeepSeek-V4-ProGLM-5.1
SWE-bench Verified58.6%80.6%57.0%
Terminal-Bench 2.082.7%67.9%—
HumanEval pass@1—76.8%—
Codeforces—3206—

Verdict:

  • DeepSeek-V4-Pro leads on SWE-bench Verified — great for full codebase analysis
  • GPT-5.5 dominates Terminal-Bench — terminal control is its strength
  • GLM-5.1 performs steadily on Chinese-language code comments and docs

2.2 Long Context Performance

All three claim long context support, but real-world results differ:

DeepSeek-V4 impressed us most: 1M token single-shot input with strong accuracy on long documents. Cross-file code analysis works reliably.

GLM-5.1 and its 128K context handles long single files fine, but analyzing an entire repo is a stretch.

GPT-5.5 offers 400K–1M context options, but the cost-to-performance ratio for ultra-long texts doesn’t match DeepSeek-V4.

2.3 Pricing Breakdown

Here’s the bottom line:

ModelInput (per 1M tokens)Output (per 1M tokens)
DeepSeek-V4-Pro$1.74$3.48
DeepSeek-V4-Flash$0.14$0.28
GLM-5.1TBATBA
GPT-5.5$5$30

DeepSeek-V4-Flash is absurdly cheap — orders of magnitude less than GPT-5.5.

3. Which Model Should You Pick?

Go with DeepSeek-V4 if:

  1. Budget is tight but you need power: V4-Flash costs about 1% of GPT-5.5 but handles everyday对话 and coding tasks just fine
  2. Private deployment is required: MIT license means deploy wherever you want
  3. Long document processing is your thing: 1M context — dump in a full technical spec and analyze it directly
  4. You’re chasing value: V4-Pro matches or beats GPT-5.5 on multiple benchmarks

Go with GLM-5.1 if:

  1. Your work is primarily in Chinese: Zhipu’s Chinese optimizations run deep
  2. You need 8+ hour task continuity: GLM-5.1’s marketed 8-hour capability is a differentiator
  3. Enterprise coding assistance matters: Integrates smoothly with existing workflows

Go with GPT-5.5 if:

  1. You need the absolute best performance: Terminal-Bench 82.7% is untouchable right now
  2. You rely on mature tooling: OpenAI’s ecosystem is still the most complete
  3. Complex Agent tasks are your core use case: Where strong terminal control is non-negotiable

4. The Surprising Takeaways

We expected GPT-5.5 to dominate across the board. The results told a different story:

  1. DeepSeek-V4-Pro actually wins at codebase analysis — SWE-bench Verified 80.6% vs 58.6% is a substantial gap
  2. GPT-5.5’s real edge is terminal control — that’s where it actually dominates
  3. The price gap is massive — GPT-5.5 costs tens of times more, but doesn’t deliver tens of times the performance
  4. Open-source models are closing in fast — DeepSeek-V4 can genuinely compete with closed-source flagships

Bottom line: unless you have a strong need for terminal control, DeepSeek-V4 is the smarter choice.

5. Try It Yourself

Saw the comparisons and want to experience DeepSeek-V4 firsthand? Click below to get started:

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Disclaimer: Benchmark data comes from public evaluation sets. Actual performance may vary by use case. Pricing reflects official announcements.

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