Ember-1 vs GLM 5.3 Flash
GLM 5.3 Flash leads Ember-1 on coding, 57.7 to 52.6 on the Vibe Coding Index, and GLM 5.3 Flash costs 30.0x less per output token ($0.50 vs $15.00 per million). Updated October 21, 2026; 10 benchmarks have a result for both.
Ember-1 or GLM 5.3 Flash: which to pick
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 52.6 for Ember-1.
Pick Ember-1 for work measured by Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
- Shared results
- 10
- Ember-1 only
- 4
- GLM 5.3 Flash only
- 92
- Like-for-like categories
- 2 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GLM 5.3 Flash
GLM 5.3 Flash scores 57.7 against 52.6.
2 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GLM 5.3 Flash has a AA Intelligence Index result, 41.8.
Directional only
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
Ember-1 $0.011, GLM 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GLM 5.3 Flash 57.7 from 6 benchmarks.
- Widest category
- Coding, 19.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Ember-1 83.9% against GLM 5.3 Flash 30.8%, 53.1 points apart on a 0 to 100 scale.
- Output price
- Ember-1 $15.00 against GLM 5.3 Flash $0.50 per million output tokens, 30.0x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Ember-1 | Bars | GLM 5.3 Flash | Ties |
|---|---|---|---|---|
| Coding | 3 | 2 | ||
| Agentic work | 2 | 3 | ||
| All shared boards | 5 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 80.6% | Only GLM 5.3 Flash measured |
| LMArena WebDev | Not measured | 1607 | Only GLM 5.3 Flash measured |
| AA Coding Index | Not measured | 71.5 | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 92.0% | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 20.7% | 37.4% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 83.9% | 30.8% | Ember-1 |
| Code migration (Vals AI) | 32.5% | 20.5% | Ember-1 |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 46.3% | 52.5% | GLM 5.3 Flash |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 62.9% | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 16.0% | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | Not measured | 40.2% | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 69.5% | 67.9% | Ember-1 |
| SciCode | Not measured | 51.6% | Only GLM 5.3 Flash measured |
| FrontierCode | Not measured | 31.8% | Only GLM 5.3 Flash measured |
| FrontierSWE | Not measured | 18.1% | Only GLM 5.3 Flash measured |
| ALE-Bench | Not measured | 303.6 | Only GLM 5.3 Flash measured |
| LMArena Text: coding | Not measured | 1507 | Only GLM 5.3 Flash measured |
| LMArena Text: software and IT | Not measured | 1500 | Only GLM 5.3 Flash measured |
| LMArena WebDev: React | Not measured | 1624 | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | Not measured | 1577 | Only GLM 5.3 Flash measured |
| LMArena WebDev: from an image | Not measured | 1578 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 41.8 | Only GLM 5.3 Flash measured |
| Epoch Capabilities Index | Not measured | 153.1 | Only GLM 5.3 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.9 | Only GLM 5.3 Flash measured |
| LiveBench research | Not measured | 70.1% | Only GLM 5.3 Flash measured |
| LiveBench browser | Not measured | 84.0% | Only GLM 5.3 Flash measured |
| LiveBench long agentic | Not measured | 28.4% | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | Not measured | 0.8% | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 51.8% | 57.9% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 31.1% | 31.6% | GLM 5.3 Flash |
| Legal Research Bench (Vals AI) | 44.7% | 45.2% | GLM 5.3 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.4% | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 63.4% | 55.9% | Ember-1 |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | 2.9% | Ember-1 |
| Vals Science Index | 31.8% | Not measured | Only Ember-1 measured |
| APEX-Agents | Not measured | 52.8% | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 86.4% | Only GLM 5.3 Flash measured |
| LiveBench reasoning | Not measured | 77.6% | Only GLM 5.3 Flash measured |
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| MMMU Pro (Vals AI) | Not measured | 86.0% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.2% | Only GLM 5.3 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 39.9% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 90.2% | Only GLM 5.3 Flash measured |
| ARC-AGI | Not measured | 91.0% | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | Not measured | 65.8% | Only GLM 5.3 Flash measured |
| Chess puzzles | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| Mystery game puzzles | Not measured | 8.0% | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | Not measured | 1490 | Only GLM 5.3 Flash measured |
| LMArena Text: expert | Not measured | 1511 | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1488 | Only GLM 5.3 Flash measured |
| LMArena Vision: diagrams | Not measured | 1317 | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | Not measured | 1318 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 93.9% | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | Not measured | 17.1% | Only GLM 5.3 Flash measured |
| ProofBench | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| LMArena Text: math | Not measured | 1503 | Only GLM 5.3 Flash measured |
| LMArena Text: mathematical work | Not measured | 1497 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.1% | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | Not measured | 83.9% | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | Not measured | 75.6% | Only GLM 5.3 Flash measured |
| LMArena Text: science | Not measured | 1484 | Only GLM 5.3 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: legal and government | Not measured | 1473 | Only GLM 5.3 Flash measured |
| LMArena Text: business and finance | Not measured | 1460 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1272 | Only GLM 5.3 Flash measured |
| Design Arena: UI components | Not measured | 1316 | Only GLM 5.3 Flash measured |
| Design Arena: game dev | Not measured | 1278 | Only GLM 5.3 Flash measured |
| Design Arena: data viz | Not measured | 1261 | Only GLM 5.3 Flash measured |
| Design Arena: 3D | Not measured | 1316 | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | Not measured | 1282 | Only GLM 5.3 Flash measured |
| Design Arena: SVG | Not measured | 1284 | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | Not measured | 1268 | Only GLM 5.3 Flash measured |
| LiveBench design | Not measured | 77.2% | Only GLM 5.3 Flash measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index29 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Vals Index | Not measured | 50.9% | Only GLM 5.3 Flash measured |
| LiveBench data analysis | Not measured | 76.4% | Only GLM 5.3 Flash measured |
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only GLM 5.3 Flash measured |
| Long context reasoning (AA-LCR) | Not measured | 80.0% | Only GLM 5.3 Flash measured |
| Surface Evolver Bench | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| LMArena Text | Not measured | 1471 | Only GLM 5.3 Flash measured |
| LMArena Text: instruction following | Not measured | 1478 | Only GLM 5.3 Flash measured |
| LMArena Text: creative writing | Not measured | 1441 | Only GLM 5.3 Flash measured |
| LMArena Vision | Not measured | 1294 | Only GLM 5.3 Flash measured |
| LMArena Text: English | Not measured | 1474 | Only GLM 5.3 Flash measured |
| LMArena Text: non-English | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Chinese | Not measured | 1531 | Only GLM 5.3 Flash measured |
| LMArena Text: German | Not measured | 1462 | Only GLM 5.3 Flash measured |
| LMArena Text: Japanese | Not measured | 1444 | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | Not measured | 1436 | Only GLM 5.3 Flash measured |
| LMArena Text: French | Not measured | 1491 | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | Not measured | 1475 | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | Not measured | 1465 | Only GLM 5.3 Flash measured |
| LMArena Text: Polish | Not measured | 1450 | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | Not measured | 1469 | Only GLM 5.3 Flash measured |
| LMArena Text: longer queries | Not measured | 1481 | Only GLM 5.3 Flash measured |
| LMArena Text: writing and language | Not measured | 1455 | Only GLM 5.3 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1440 | Only GLM 5.3 Flash measured |
| LMArena Vision: English | Not measured | 1295 | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | Not measured | 1363 | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | Not measured | 1299 | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | Not measured | 1279 | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | Not measured | 1270 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ember-1 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Fireworks Ember-1 | Z.ai GLM 5.3 Flash |
|---|---|---|
| Maker | Fireworks | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | August 26, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $3.00 | $0.15 |
| Output $/Mtok | $15.00 | $0.50 |
| Coding index | 52.6 | 57.7 |
| Independent measurements behind it | 2 | 6 |
| Cost per solved task | Not computable | $0.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than GLM 5.3 Flash for coding?
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 52.6 for Ember-1. Pick Ember-1 for work measured by Vibe Code Bench (Vals AI) or Code migration (Vals AI), where it leads.
Which is cheaper, Ember-1 or GLM 5.3 Flash?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, Ember-1 or GLM 5.3 Flash?
Neither: both take 1.05M tokens.
How many benchmarks is the Ember-1 vs GLM 5.3 Flash comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. Ember-1's coding index rests on 2 independent benchmarks and GLM 5.3 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.