deepseek-flash vs glm-5.3-flash

Parameters, independent leaderboard coverage, real-task reports and open-weight availability are shown side by side; missing coverage is not a zero.

Parameters

Comparison itemdeepseek-flashglm-5.3-flash
Model TypeLLMLLM
Context window1,000,000 tokens1,000,000 tokens
Maximum output384,000 tokens128,000 tokens
Input/output modalitiestext, image → texttext, image, video → text
Endpointopenai · openai-response · anthropicopenai · anthropic
Open weightsAvailableAvailable
LicenseMITMIT

Leaderboard coverage

Every independent board covering either model is retained; an em dash means that board has not published a figure for that model.

A row marked as retrieved on different dates holds two readings taken on different days; a board re-fits or changes version as a whole between readings, so those two figures are not directly comparable.

Evaluationdeepseek-flashglm-5.3-flash
LMArena Text Arena — Arena score (Elo)1475 ±7
LiveBench — Overall81.171.6
Artificial Analysis Intelligence Index — Intelligence Index3942
SuperCLUE 智能指数 — 总分 (overall)71.8168.10
Epoch Capabilities Index (ECI) — General ECI155 (149 - 158)152 (150 - 154)
Vals Index — Accuracy (GDP-weighted finance, coding and legal)
Retrieved on different dates
57.86 ±1.16 (retrieved 2026-09-21)47.22 ±1.45 (retrieved 2026-09-22)
LMArena Text Arena · Chinese — Arena score (Elo)1529 ±25
LMArena Text Arena · Coding — Arena score (Elo)1525 ±12
LiveBench · Coding — Coding80.079.0
LiveBench · Agentic Coding — Agentic Coding77.356.8
Artificial Analysis · Output Speed — Median output tokens/s22065
Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%)5557
Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%)2733
Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%)
Retrieved on different dates
22.8 (retrieved 2026-09-22)51.1 (retrieved 2026-09-07)

Real-task reports

deepseek-flash

  • RemakeBench Launch 005: four Flash models on 14 fixtures — Fourteen fixtures spanning game building, 3D scenes, CAD, drawing and robot control, run on Qwen 3.8 Flash, DeepSeek V4.1 Flash, GLM 5.3 Flash and Gemini 3.8 Flash. Finding: No model won overall. DeepSeek V4.1 Flash delivered the best submitted Turbofan CAD result (three valid native solids, with a wrong-sign coupling and LP/HP interference) and did not pass the single-turn Rubik's Cube task. Limitation: Pass/fail labels are video judgments without a machine-readable validator, and single attempts do not establish reliability; the lab publishes no aggregate score or overall winner. Several DeepSeek clips reuse earlier records that keep V4 labels and the Vision exp setting; the two results quoted here are campaign runs added for this comparison.
  • MiMo-V2.6-Flash, DeepSeek V4.1 Flash and Grok 4.7 on one game prompt — Build a playable browser game from a single prompt with Command Code's /design command. Finding: DeepSeek V4.1 Flash scored 9/10 for $0.0089 with a playable one-shot game. MiMo-V2.6-Flash also scored 9/10 at $0.005; Grok 4.7 scored 8/10 at $0.20 and needed iterations. Limitation: One prompt rated by the author, whose profile lists work for Command Code, the tool that ran the test; there is no rubric and no repeated run.
  • DeepSeek V4.1 Flash and GLM 5.3 Flash recreating two game menus — Recreate the Psychonauts 2 and Metroid Prime main menus as closely as possible to the originals. Finding: DeepSeek V4.1 Flash used 412k and 466k tokens, three to four times GLM 5.3 Flash's 134k and 111k, and the author judged GLM's menus clearly better on both games. Limitation: Which menu is closer to the original is the author's own judgement on two prompts, without a scoring rubric.
  • Switching a coding project from GPT-6 Astra to DeepSeek V4.1 Flash — Review an existing project, list its issues and fix them. Finding: DeepSeek V4.1 Flash found several issues, asked which to tackle first and began fixing them; Kimi's review still found problems, which were passed back to DeepSeek to correct. Limitation: One uncontrolled self-report. The author ran DeepSeek through WorkBuddy's free access, so the remark about spending nothing extra does not reflect API pricing.
  • Step 5 Preview and DeepSeek V4.1 Flash on a 3D arcade game — Build Katamari Tiny, a 3D arcade game, from a single prompt. Finding: DeepSeek V4.1 Flash used 26,898 tokens against Step 5 Preview's 39,830 and, in the author's view, was faster and produced the better game. Limitation: One prompt judged by an author who names DeepSeek V4.1 Flash as their default model; the post gives no timings or quality rubric.
  • DeepSeek V4.1 Flash and GLM-5.3-Flash on a Blender geometry task, run locally — Write a Blender script that builds a dodecahedron trapped inside a dodecahedron. Finding: Both models produced a correct mesh with zero intersections. DeepSeek V4.1 Flash took 20 min 15 s and 62,928 tokens at 51.8 tok/s, about a fifth of GLM-5.3-Flash's time and half its tokens, and its computed clearance matched the measured mesh (0.238); the author found GLM's picture nicer. Limitation: One local run per model on different hardware and precision: GLM ran as a third-party EXL3 quantisation on two DGX Sparks and DeepSeek as FP8 on four, so speed and token counts describe those builds, not the official weights or a hosted API.
  • Three Flash models build the same koi pond locally — A koi pond build, run as a local head-to-head. Finding: All three finished cleanly with zero fixes. DeepSeek V4.1 Flash was fastest at 21 minutes and 53.3K tokens, a third of GLM-5.3-Flash's time, and the author ranked it first. Limitation: One local run per model; the post states neither the prompt nor the quantisation or runtime used.
  • Step 5 Preview and DeepSeek V4.1 Flash on one Three.js scene — A Three.js scene of giant red mesas in a desert, with long shadows and a huge empty sky. Finding: DeepSeek V4.1 Flash's scene was more restrained but had clearly better ground and mesa textures and a smoother camera; Step 5 Preview added more atmosphere, a tracked sun, particle and dust effects and on-screen text. Limitation: One prompt judged by eye by the author; the post does not say how either model was accessed.

glm-5.3-flash

  • RemakeBench Launch 005: four Flash models on 14 fixtures — Fourteen fixtures spanning game building, 3D scenes, CAD, drawing and robot control, run on Qwen 3.8 Flash, DeepSeek V4.1 Flash, GLM 5.3 Flash and Gemini 3.8 Flash. Finding: No model won overall. GLM 5.3 Flash was the only model to pass the Apple-stem manipulation task and passed the single-turn Rubik's Cube, but did not complete the three-move extension and delivered no native geometry in Turbofan CAD. Limitation: Pass/fail labels are video judgments without a machine-readable validator, and single attempts do not establish reliability; the lab publishes no aggregate score or overall winner. GLM's Infinite Cathedral completion needed repeated user continuations because the endpoint stalled.
  • GLM 5.3 Flash alone and with Jev on Halite 2 — Play the turn-based strategy game Halite 2. Finding: GLM 5.3 Flash alone beat Jev alone 82% of the time; the hybrid was 13× faster at 56% of the pure GLM API cost with a slight performance edge. Limitation: The author calls it a single example and among their first experiments with Jev; results may not carry over to other tasks.
  • DeepSeek V4.1 Flash and GLM 5.3 Flash recreating two game menus — Recreate the Psychonauts 2 and Metroid Prime main menus as closely as possible to the originals. Finding: GLM 5.3 Flash used 134k and 111k tokens, a third to a quarter of DeepSeek V4.1 Flash's, and the author judged its menus clearly better on both games. Limitation: Which menu is closer to the original is the author's own judgement on two prompts, without a scoring rubric.
  • DeepSeek V4.1 Flash and GLM-5.3-Flash on a Blender geometry task, run locally — Write a Blender script that builds a dodecahedron trapped inside a dodecahedron. Finding: GLM-5.3-Flash produced a correct mesh (zero intersections, containment verified in-script) and the nicer picture, but took 93 min 12 s and 132,250 tokens at 23.7 tok/s, against 20 min 15 s and 62,928 tokens for DeepSeek V4.1 Flash. Limitation: One local run per model on different hardware and precision: GLM ran as a third-party EXL3 quantisation on two DGX Sparks and DeepSeek as FP8 on four, so speed and token counts describe those builds, not the official weights or a hosted API.
  • Three Flash models build the same koi pond locally — A koi pond build, run as a local head-to-head. Finding: All three finished cleanly with zero fixes, but GLM-5.3-Flash was slowest at 59 minutes and 90.9K tokens, against 30 minutes for MiMo-V2.6-Flash and 21 for DeepSeek V4.1 Flash; the author said it was the first of their tests where GLM was not the winner. Limitation: One local run per model; the post states neither the prompt nor the quantisation or runtime used.