Jev vs GLM 5.3
At a Glance
| Compare |  JevTypeSafe |  GLM 5.3Z.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #34 of 44$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.042TypeSafe ↗ · Sep 17, 2026 | $1.20Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.000TypeSafe ↗ · Sep 17, 2026 | $4.00Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 64K | 1,000K |
| Model facts checked | Sep 17, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Jev | GLM-5.3 |
|---|---|---|
| Developer | TypeSafe | Z.ai |
| Family | Jev | Glm 5 3 |
| Model | Jev | GLM-5.3 |
| Version | Jev | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2026-09-15 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Model-specific input | Text |
| Output modalities | Model-specific input | Text |
| Context window | 64K | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | TypeSafe (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions | agents, chat, reasoning, structured_outputs, tools |
| Maximum Choice cardinality | 255 options | Unknown |
| Default request rate limit | 1200 requests per minute | Unknown |
| State plus longest question limit | 32000 tokens | Unknown |
| Combined state and questions limit | 64000 tokens | Unknown |
| Default token rate limit | 250000 tokens per second | Unknown |
Jev Capabilities
GLM 5.3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
JevTypeSafe | vs | GLM-5.2Z.ai | cross-developer peerstext |
 GPT-6 AstraOpenAI | vs | JevTypeSafe | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | JevTypeSafe | cross-developer peerstext |
 Gemini 3.1 ProGoogle DeepMind | vs | JevTypeSafe | cross-developer peerstext |
 DeepSeek-V4.1-FlashDeepSeek | vs | JevTypeSafe | cross-developer peerstext |
JevTypeSafe | vs | Grok 4.6xAI | cross-developer peerstext |
 Qwen3.8-MaxQwen | vs | JevTypeSafe | cross-developer peerstext |
 Kimi-K3Moonshot AI | vs | JevTypeSafe | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | JevTypeSafe | cross-developer peerstext |
 Hy4 previewTencent | vs | JevTypeSafe | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | JevTypeSafe | cross-developer peerstext |
 Mistral Large 3Mistral AI | vs | JevTypeSafe | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Jev vs GLM 5.3 FAQs
Is Jev or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Jev and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Jev or GLM 5.3?+
Jev is $0.042 and GLM 5.3 is $1.20 per million tokens, so Jev is cheaper on this metric. Jev is $0.000 and GLM 5.3 is $4.00 per million tokens, so Jev is cheaper on this metric.
Which has a larger context window, Jev or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Jev supports 64K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Jev or GLM 5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Jev or GLM 5.3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Jev is not marked open weight; GLM 5.3 is not marked open weight.
Can Jev and GLM 5.3 understand images?+
Jev is not documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Jev or GLM 5.3?+
Neither has a larger sourced maximum output. Jev is — and GLM 5.3 is 131K.
Do Jev and GLM 5.3 support reasoning and tool use?+
Jev: none of these features are definitively sourced. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Jev or GLM 5.3?+
Jev has 1 sourced provider route; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Jev or GLM 5.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.