Llama 3.3 70B Instruct vs Perceptron Mk1.5
Llama 3.3 70B Instruct costs 2.1x less per output token than Perceptron Mk1.5 ($0.72 vs $1.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 26.0
- Output $/M
- $0.72
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
Llama 3.3 70B Instruct or Perceptron Mk1.5: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 2.1x cheaper ($0.72 vs $1.50 per million) and a larger context window (131K against 37K tokens).
No benchmark has tested both yet. Llama 3.3 70B Instruct has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Perceptron Mk1.5 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Llama 3.3 70B Instruct has a AA Intelligence Index result, 7.7.
Directional only
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 37K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Perceptron Mk1.5 $0.0009 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Perceptron Mk1.5 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
- Llama 3.3 70B Instruct $0.72 against Perceptron Mk1.5 $1.50 per million output tokens, 2.1x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Perceptron Mk1.5 37K.
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 Index6 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | 1273 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 31.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | 1225 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | 1277 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | 1243 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | Llama 3.3 70B Instruct | Perceptron Mk1.5 | Leader |
|---|---|---|---|
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1274 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | 1242 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | 1250 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | 1303 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | 1236 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | 1217 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | 1251 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | 1269 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1252 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | 1241 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 24 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
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Perceptron Mk1.5 costs less
Agent loop
200K input + 10K output tokens
Perceptron Mk1.5 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 | Meta Llama 3.3 70B Instruct | Perceptron Mk1.5 |
|---|---|---|
| Maker | Meta Llama | Perceptron |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | September 25, 2026 |
| Context window | 131K | 37K |
| Input $/Mtok | $0.45 | $0.15 |
| Output $/Mtok | $0.72 | $1.50 |
| 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 Llama 3.3 70B Instruct better than Perceptron Mk1.5 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 2.1x cheaper ($0.72 vs $1.50 per million) and a larger context window (131K against 37K tokens).
Which is cheaper, Llama 3.3 70B Instruct or Perceptron Mk1.5?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Perceptron Mk1.5 costs $0.15 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Perceptron Mk1.5?
Llama 3.3 70B Instruct, with 131K tokens against 37K.
Has any benchmark tested both Llama 3.3 70B Instruct and Perceptron Mk1.5?
No benchmark has tested both yet. Llama 3.3 70B Instruct has results on AA Coding Index, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Perceptron Mk1.5 has none yet. Compare them on price and context instead. Data as of October 22, 2026.