GLM 4.6V vs Hermes 3 70B Instruct
GLM 4.6V leads Hermes 3 70B Instruct on the Artificial Analysis Intelligence Index, 11.2 to 6.7, and Hermes 3 70B Instruct costs 1.3x less per output token ($0.70 vs $0.90 per million). Updated October 23, 2026; 5 benchmarks have a result for both.
GLM 4.6V or Hermes 3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 5
- GLM 4.6V only
- 33
- Hermes 3 70B Instruct only
- 0
- Like-for-like categories
- 4 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6V
GLM 4.6V leads, 11.2 against 6.7.
Same board, both measured
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, Hermes 3 70B Instruct $0.0010 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Hermes 3 70B Instruct not benchmarked.
- Widest category
- Knowledge, 22.8 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GLM 4.6V 71.9% against Hermes 3 70B Instruct 40.1%, 31.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Hermes 3 70B Instruct $0.70 per million output tokens, 1.3x apart.
- Context window
- Both take 131K 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 | GLM 4.6V | Bars | Hermes 3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 5 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | 18.8% | GLM 4.6V |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: software and IT | 1391 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 6.7 | GLM 4.6V |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 40.1% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 4.0% | GLM 4.6V |
| LMArena Text: hard prompts | 1368 | Not measured | Only GLM 4.6V measured |
| LMArena Text: expert | 1371 | Not measured | Only GLM 4.6V measured |
| LMArena Text: hard prompts in English | 1391 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | 57.1% | GLM 4.6V |
| LMArena Text: science | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: medicine and healthcare | 1389 | Not measured | Only GLM 4.6V measured |
| LMArena Text: legal and government | 1393 | Not measured | Only GLM 4.6V measured |
| LMArena Text: business and finance | 1359 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | Not measured | Only GLM 4.6V measured |
| LMArena Text | 1376 | Not measured | Only GLM 4.6V measured |
| LMArena Text: instruction following | 1350 | Not measured | Only GLM 4.6V measured |
| LMArena Text: creative writing | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | Not measured | Only GLM 4.6V measured |
| LMArena Text: non-English | 1359 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Chinese | 1425 | Not measured | Only GLM 4.6V measured |
| LMArena Text: Russian | 1339 | Not measured | Only GLM 4.6V measured |
| LMArena Text: multi-turn | 1359 | Not measured | Only GLM 4.6V measured |
| LMArena Text: longer queries | 1357 | Not measured | Only GLM 4.6V measured |
| LMArena Text: writing and language | 1347 | Not measured | Only GLM 4.6V measured |
| LMArena Text: entertainment and media | 1343 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Hermes 3 70B Instruct | 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 4.6V costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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 | Z.ai GLM 4.6V | Nous Hermes 3 70B Instruct |
|---|---|---|
| Maker | Z.ai | Nous Research |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | August 18, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.30 | $0.70 |
| Output $/Mtok | $0.90 | $0.70 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Hermes 3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GLM 4.6V or Hermes 3 70B Instruct?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Hermes 3 70B Instruct costs $0.70 and $0.70. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Hermes 3 70B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.6V vs Hermes 3 70B Instruct comparison based on?
5 benchmarks have a result for both models as of October 23, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Hermes 3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.