gemini-3.8-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 item | gemini-3.8-flash | glm-5.3-flash |
|---|---|---|
| Model Type | LLM | LLM |
| Context window | 1,048,576 tokens | 1,000,000 tokens |
| Maximum output | 65,536 tokens | 128,000 tokens |
| Input/output modalities | text, image, video, audio, pdf → text | text, image, video → text |
| Endpoint | openai · gemini | openai · anthropic |
| Open weights | Unavailable | Available |
| License | Proprietary hosted model | MIT |
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.
| Evaluation | gemini-3.8-flash | glm-5.3-flash |
|---|---|---|
| LMArena Text Arena — Arena score (Elo) | 1493 ±9 | 1475 ±7 |
| LiveBench — Overall | 75.8 | 71.6 |
| Artificial Analysis Intelligence Index — Intelligence Index | 41 | 42 |
| SuperCLUE 智能指数 — 总分 (overall) | — | 68.10 |
| Epoch Capabilities Index (ECI) — General ECI | 157 (155 - 161) | 152 (150 - 154) |
| Vals Index — Accuracy (GDP-weighted finance, coding and legal) Retrieved on different dates | 62.25 ±1.01 (retrieved 2026-09-21) | 47.22 ±1.45 (retrieved 2026-09-22) |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1543 ±32 | 1529 ±25 |
| LMArena Text Arena · Coding — Arena score (Elo) | 1535 ±16 | 1525 ±12 |
| LiveBench · Coding — Coding | 72.5 | 79.0 |
| LiveBench · Agentic Coding — Agentic Coding | 54.2 | 56.8 |
| Artificial Analysis · Output Speed — Median output tokens/s | 297 | 65 |
| Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%) | 46 | 57 |
| Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%) | 20 | 33 |
| Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%) | 21.7 | 51.1 |
Real-task reports
gemini-3.8-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. Gemini 3.8 Flash made the fastest single-turn Rubik's Cube pass (15 min 35 s, $2.09) and was the editorial winner of the robot Eiffel Tower drawing (8 min 25 s, $1.36), but did not complete the three-move extension ($5.65) and did not deliver the required Turbofan CAD assembly. 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. Gemini's displayed costs are several times the other models' on most fixtures (for example $2.09 against $0.0957–$0.3414 on the single-turn cube), and its CAD presentation clip is operator work after the run.
- Cortex AI: GPT-6 Astra, Gemini 3.8 Flash and Fable 5.1 on two real robot tasks — Load a sandwich into a microwave and close the door; attach a 5 mm tip to a pipette. Finding: Gemini 3.8 Flash scored 13.3% on the microwave task and 30% on the pipette task, second to GPT-6 Astra (80% and 70%) and ahead of Fable 5.1 (6.7% and 20%). Limitation: The post does not say how many attempts each model had; scores are partial-credit judgements by the lab's evaluator, and the authors attribute the gap mainly to weaker depth perception in the lower-scoring models.
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.