Ember-1 vs GLM 4.5
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- 12.8
- Output $/M
- $2.20
Decision reading
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.
GLM 4.5 has the edge where output tokens cost 6.8x less.
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); GLM 4.5 on SWE-bench Verified, LiveCodeBench (Vals AI) and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead.
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
No call
Only Ember-1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5 has a AA Intelligence Index result, 12.8.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1170.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
Ember-1 $0.011, GLM 4.5 $0.0017 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GLM 4.5 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Ember-1 $15.00 against GLM 4.5 $2.20 per million output tokens, 6.8x apart.
- Context window
- Ember-1 takes 1.05M tokens, GLM 4.5 131K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 64.2% | Only GLM 4.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 67.5% | Only GLM 4.5 measured |
| Terminal-Bench 4 (Vals AI) | 20.7% | Not measured | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | 83.9% | Not measured | Only Ember-1 measured |
| Code migration (Vals AI) | 32.5% | Not measured | Only Ember-1 measured |
| IOI (Vals AI) | 46.3% | Not measured | Only Ember-1 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Ember-1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 73.8% | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 22.0% | Only GLM 4.5 measured |
| WeirdML | Not measured | 40.6% | Only GLM 4.5 measured |
| ALE-Bench | Not measured | 344.8 | Only GLM 4.5 measured |
| LMArena Text: coding | Not measured | 1434 | Only GLM 4.5 measured |
| LMArena Text: software and IT | Not measured | 1440 | Only GLM 4.5 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.8 | Only GLM 4.5 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 51.8% | Not measured | Only Ember-1 measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only Ember-1 measured |
| Legal Research Bench (Vals AI) | 44.7% | Not measured | Only Ember-1 measured |
| Excel Modeling Benchmark (Vals AI) | 63.4% | Not measured | Only Ember-1 measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Ember-1 measured |
| Vals Science Index | 31.8% | Not measured | Only Ember-1 measured |
| τ²-Bench Telecom | Not measured | 43.0% | Only GLM 4.5 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 72.2% | Only GLM 4.5 measured |
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 78.2% | Only GLM 4.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 13.0% | Only GLM 4.5 measured |
| LMArena Text: hard prompts | Not measured | 1429 | Only GLM 4.5 measured |
| LMArena Text: expert | Not measured | 1433 | Only GLM 4.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1429 | Only GLM 4.5 measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 86.7% | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | Not measured | 94.0% | Only GLM 4.5 measured |
| MGSM (Vals AI) | Not measured | 90.8% | Only GLM 4.5 measured |
| AA Math Index | Not measured | 73.7 | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.7% | Only GLM 4.5 measured |
| LMArena Text: math | Not measured | 1427 | Only GLM 4.5 measured |
| LMArena Text: mathematical work | Not measured | 1430 | Only GLM 4.5 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 81.2% | Only GLM 4.5 measured |
| LegalBench (Vals AI) | Not measured | 75.6% | Only GLM 4.5 measured |
| CorpFin (Vals AI) | Not measured | 61.0% | Only GLM 4.5 measured |
| TaxEval (Vals AI) | Not measured | 72.4% | Only GLM 4.5 measured |
| MedQA (Vals AI) | Not measured | 90.0% | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 83.5% | Only GLM 4.5 measured |
| LMArena Text: science | Not measured | 1444 | Only GLM 4.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1455 | Only GLM 4.5 measured |
| LMArena Text: legal and government | Not measured | 1438 | Only GLM 4.5 measured |
| LMArena Text: business and finance | Not measured | 1420 | Only GLM 4.5 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1170 | Only GLM 4.5 measured |
| Design Arena: UI components | Not measured | 1144 | Only GLM 4.5 measured |
| Design Arena: game dev | Not measured | 1145 | Only GLM 4.5 measured |
| Design Arena: data viz | Not measured | 1159 | Only GLM 4.5 measured |
| Design Arena: 3D | Not measured | 1170 | Only GLM 4.5 measured |
| Design Arena: all code categories | Not measured | 1165 | Only GLM 4.5 measured |
| Design Arena: SVG | Not measured | 1096 | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards measured
| Benchmark | Ember-1 | GLM 4.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 44.1% | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 52.7% | Only GLM 4.5 measured |
| Creative writing (Lech Mazur) | Not measured | 73.4% | Only GLM 4.5 measured |
| LMArena Text | Not measured | 1430 | Only GLM 4.5 measured |
| LMArena Text: instruction following | Not measured | 1404 | Only GLM 4.5 measured |
| LMArena Text: creative writing | Not measured | 1395 | Only GLM 4.5 measured |
| LMArena Text: English | Not measured | 1437 | Only GLM 4.5 measured |
| LMArena Text: non-English | Not measured | 1417 | Only GLM 4.5 measured |
| LMArena Text: Chinese | Not measured | 1465 | Only GLM 4.5 measured |
| LMArena Text: German | Not measured | 1407 | Only GLM 4.5 measured |
| LMArena Text: Japanese | Not measured | 1415 | Only GLM 4.5 measured |
| LMArena Text: Korean | Not measured | 1380 | Only GLM 4.5 measured |
| LMArena Text: French | Not measured | 1418 | Only GLM 4.5 measured |
| LMArena Text: Spanish | Not measured | 1454 | Only GLM 4.5 measured |
| LMArena Text: Russian | Not measured | 1413 | Only GLM 4.5 measured |
| LMArena Text: Polish | Not measured | 1422 | Only GLM 4.5 measured |
| LMArena Text: multi-turn | Not measured | 1415 | Only GLM 4.5 measured |
| LMArena Text: longer queries | Not measured | 1412 | Only GLM 4.5 measured |
| LMArena Text: writing and language | Not measured | 1403 | Only GLM 4.5 measured |
| LMArena Text: entertainment and media | Not measured | 1404 | Only GLM 4.5 measured |
| Neither model has a result on the other 25 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 4.5 |
|---|---|---|
| Maker | Fireworks | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | July 25, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $3.00 | $0.60 |
| Output $/Mtok | $15.00 | $2.20 |
| Coding index | 52.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than GLM 4.5 for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. GLM 4.5 has the edge where output tokens cost 6.8x less.
Which is cheaper, Ember-1 or GLM 4.5?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM 4.5 costs $0.60 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GLM 4.5?
Ember-1, with 1.05M tokens against 131K.
Has any benchmark tested both Ember-1 and GLM 4.5?
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); GLM 4.5 on SWE-bench Verified, LiveCodeBench (Vals AI) and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.