Ember-1 vs GLM 4.7 Flash
GLM 4.7 Flash costs 37.5x less per output token than Ember-1 ($0.40 vs $15.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- 14.9
- Output $/M
- $0.40
Ember-1 or GLM 4.7 Flash: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 37.5x cheaper ($0.40 vs $15.00 per million).
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 4.7 Flash on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. 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 4.7 Flash has a AA Intelligence Index result, 14.9.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
Ember-1 $0.011, GLM 4.7 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GLM 4.7 Flash 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 4.7 Flash $0.40 per million output tokens, 37.5x apart.
- Context window
- Ember-1 takes 1.05M tokens, GLM 4.7 Flash 200K.
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 Index8 boards measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| 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 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 22.0% | Only GLM 4.7 Flash measured |
| LMArena Text: coding | Not measured | 1382 | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | Not measured | 1389 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 14.9 | Only GLM 4.7 Flash 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 4.7 Flash | 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 | 98.8% | Only GLM 4.7 Flash 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 4.7 Flash | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 58.1% | Only GLM 4.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 7.6% | Only GLM 4.7 Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 60.5% | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | Not measured | 1356 | Only GLM 4.7 Flash measured |
| LMArena Text: expert | Not measured | 1357 | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1369 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 58.3% | Only GLM 4.7 Flash measured |
| LMArena Text: math | Not measured | 1355 | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | Not measured | 1369 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1358 | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1343 | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | Not measured | 1357 | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | Not measured | 1343 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1194 | Only GLM 4.7 Flash measured |
| Design Arena: UI components | Not measured | 1208 | Only GLM 4.7 Flash measured |
| Design Arena: game dev | Not measured | 1135 | Only GLM 4.7 Flash measured |
| Design Arena: data viz | Not measured | 1123 | Only GLM 4.7 Flash measured |
| Design Arena: 3D | Not measured | 1121 | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | Not measured | 1178 | Only GLM 4.7 Flash measured |
| Design Arena: SVG | Not measured | 1038 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards measured
| Benchmark | Ember-1 | GLM 4.7 Flash | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 60.8% | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | Not measured | 41.7% | Only GLM 4.7 Flash measured |
| LMArena Text | Not measured | 1351 | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | Not measured | 1327 | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | Not measured | 1296 | Only GLM 4.7 Flash measured |
| LMArena Text: English | Not measured | 1373 | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | Not measured | 1330 | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | Not measured | 1403 | Only GLM 4.7 Flash measured |
| LMArena Text: German | Not measured | 1336 | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | Not measured | 1283 | Only GLM 4.7 Flash measured |
| LMArena Text: French | Not measured | 1332 | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | Not measured | 1350 | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | Not measured | 1333 | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | Not measured | 1291 | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | Not measured | 1342 | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | Not measured | 1345 | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | Not measured | 1320 | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1304 | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 27 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 4.7 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 Flash |
|---|---|---|
| Maker | Fireworks | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | January 19, 2026 |
| Context window | 1.05M | 200K |
| Input $/Mtok | $3.00 | $0.0653 |
| Output $/Mtok | $15.00 | $0.40 |
| 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 4.7 Flash for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 37.5x cheaper ($0.40 vs $15.00 per million).
Which is cheaper, Ember-1 or GLM 4.7 Flash?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM 4.7 Flash costs $0.0653 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GLM 4.7 Flash?
Ember-1, with 1.05M tokens against 200K.
Has any benchmark tested both Ember-1 and GLM 4.7 Flash?
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 4.7 Flash on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead. Data as of October 21, 2026.