Ember-1 vs GLM 5.3 Prime
GLM 5.3 Prime costs 1.7x less per output token than Ember-1 ($8.80 vs $15.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $8.80
Ember-1 or GLM 5.3 Prime: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 5.3 Prime has none, so the two cannot be ranked on coding.
Pick GLM 5.3 Prime for output tokens 1.7x cheaper ($8.80 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 5.3 Prime has none yet. 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
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Prime
Ember-1 $0.011, GLM 5.3 Prime $0.0072 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, GLM 5.3 Prime 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 5.3 Prime $8.80 per million output tokens, 1.7x apart.
- Context window
- Ember-1 takes 1.05M tokens, GLM 5.3 Prime 1M.
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 Index5 boards measured
| Benchmark | Ember-1 | GLM 5.3 Prime | 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 |
| Neither model has a result on the other 34 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Ember-1 | GLM 5.3 Prime | 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 |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GLM 5.3 Prime | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 21 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | GLM 5.3 Prime | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| Neither model has a result on the other 45 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 Prime costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Prime costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Prime 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 Prime |
|---|---|---|
| Maker | Fireworks | Z.ai |
| Weights | No public weights listed | No public weights listed |
| Released | September 24, 2026 | September 23, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $3.00 | $2.80 |
| Output $/Mtok | $15.00 | $8.80 |
| 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 5.3 Prime for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; GLM 5.3 Prime has none, so the two cannot be ranked on coding. Pick GLM 5.3 Prime for output tokens 1.7x cheaper ($8.80 vs $15.00 per million).
Which is cheaper, Ember-1 or GLM 5.3 Prime?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM 5.3 Prime costs $2.80 and $8.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or GLM 5.3 Prime?
Ember-1, with 1.05M tokens against 1M.
Has any benchmark tested both Ember-1 and GLM 5.3 Prime?
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 5.3 Prime has none yet. Compare them on price and context instead. Data as of October 22, 2026.