Llama 3.2 3B Instruct vs Nex-N2.5-Mini
Nex-N2.5-Mini costs 3.3x less per output token than Llama 3.2 3B Instruct ($0.10 vs $0.3325 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.3325
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.10
Llama 3.2 3B Instruct or Nex-N2.5-Mini: 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.
No benchmark has tested both yet. Llama 3.2 3B Instruct has results on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; Nex-N2.5-Mini 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 3.2 3B Instruct has a AA Intelligence Index result, 5.7.
Directional only
Long documents
Listed context window
Nex-N2.5-Mini
Nex-N2.5-Mini takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nex-N2.5-Mini
Llama 3.2 3B Instruct $0.0002, Nex-N2.5-Mini $0.0001 per turn.
List prices
At a glance
- Coding index
- Llama 3.2 3B Instruct not benchmarked, Nex-N2.5-Mini 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 3.2 3B Instruct $0.3325 against Nex-N2.5-Mini $0.10 per million output tokens, 3.3x apart.
- Context window
- Llama 3.2 3B Instruct takes 131K tokens, Nex-N2.5-Mini 262K.
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 Index3 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 8.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: coding | 1098 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | 1103 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 21.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| BALROG | 10.1% | Not measured | Only Llama 3.2 3B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 21.9% | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 25.5% | Not measured | Only Llama 3.2 3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts | 1095 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | 1090 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | 1141 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| AA Math Index | 3.3 | Not measured | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 3.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: math | 1126 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | 1128 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 34.7% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | 1106 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | 1104 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | 1115 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | 1074 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards measured
| Benchmark | Llama 3.2 3B Instruct | Nex-N2.5-Mini | Leader |
|---|---|---|---|
| IFBench | 26.2% | Not measured | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | 4.3% | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text | 1110 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | 1089 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | 1094 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | 1180 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | 1019 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | 1017 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | 1056 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | 949 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | 1105 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | 1100 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | 1096 | Not measured | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | 1083 | Not measured | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 32 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
Nex-N2.5-Mini costs less
Repository review
50K input + 3K output tokens
Nex-N2.5-Mini costs less
Agent loop
200K input + 10K output tokens
Nex-N2.5-Mini 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 3.2 3B Instruct | Nex AGI Nex-N2.5-Mini |
|---|---|---|
| Maker | Meta Llama | Nex AGI |
| Weights | Open weights | Open weights |
| Released | September 25, 2024 | September 8, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.0505 | $0.025 |
| Output $/Mtok | $0.3325 | $0.10 |
| 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 3.2 3B Instruct better than Nex-N2.5-Mini 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, Llama 3.2 3B Instruct or Nex-N2.5-Mini?
Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Nex-N2.5-Mini costs $0.025 and $0.10. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.2 3B Instruct or Nex-N2.5-Mini?
Nex-N2.5-Mini, with 262K tokens against 131K.
Has any benchmark tested both Llama 3.2 3B Instruct and Nex-N2.5-Mini?
No benchmark has tested both yet. Llama 3.2 3B Instruct has results on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; Nex-N2.5-Mini has none yet. Compare them on price and context instead. Data as of October 22, 2026.