Qwen3 32B vs ReMM SLERP 13B
Qwen3 32B costs 1.6x less per output token than ReMM SLERP 13B ($0.40 vs $0.65 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 40.8
- Output $/M
- $0.40
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.65
Qwen3 32B or ReMM SLERP 13B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 32B for output tokens 1.6x cheaper ($0.40 vs $0.65 per million) and a larger context window (131K against 6K tokens).
No benchmark has tested both yet. Qwen3 32B has results on Aider polyglot, LiveCodeBench (Artificial Analysis run) and SciCode; ReMM SLERP 13B 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 Qwen3 32B has a AA Intelligence Index result, 7.3.
Directional only
Long documents
Listed context window
Qwen3 32B
Qwen3 32B takes 131K tokens against 6K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
Qwen3 32B $0.0003, ReMM SLERP 13B $0.0007 per turn.
List prices
At a glance
- Coding index
- Qwen3 32B not benchmarked, ReMM SLERP 13B 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
- Qwen3 32B $0.40 against ReMM SLERP 13B $0.65 per million output tokens, 1.6x apart.
- Context window
- Qwen3 32B takes 131K tokens, ReMM SLERP 13B 6K.
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 | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| Aider polyglot | 40.0% | Not measured | Only Qwen3 32B measured |
| LiveCodeBench (Artificial Analysis run) | 54.6% | Not measured | Only Qwen3 32B measured |
| SciCode | 36.0% | Not measured | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Qwen3 32B measured |
| LMArena Text: coding | 1357 | Not measured | Only Qwen3 32B measured |
| LMArena Text: software and IT | 1366 | Not measured | Only Qwen3 32B measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | Not measured | Only Qwen3 32B measured |
| Epoch Capabilities Index | 138.4 | Not measured | Only Qwen3 32B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 29.8% | Not measured | Only Qwen3 32B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 48.7% | Not measured | Only Qwen3 32B measured |
| BrowseComp-Plus | 10.7% | Not measured | Only Qwen3 32B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 66.8% | Not measured | Only Qwen3 32B measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.4% | Not measured | Only Qwen3 32B measured |
| GPQA Diamond (Epoch AI run) | 65.7% | Not measured | Only Qwen3 32B measured |
| LMArena Text: hard prompts | 1334 | Not measured | Only Qwen3 32B measured |
| LMArena Text: expert | 1362 | Not measured | Only Qwen3 32B measured |
| LMArena Text: hard prompts in English | 1350 | Not measured | Only Qwen3 32B measured |
| Kagi LLM Benchmark | 48.7% | Not measured | Only Qwen3 32B measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only Qwen3 32B measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only Qwen3 32B measured |
| OTIS Mock AIME | 66.9% | Not measured | Only Qwen3 32B measured |
| LMArena Text: math | 1399 | Not measured | Only Qwen3 32B measured |
| LMArena Text: mathematical work | 1420 | Not measured | Only Qwen3 32B 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 | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.8% | Not measured | Only Qwen3 32B measured |
| LMArena Text: science | 1372 | Not measured | Only Qwen3 32B measured |
| LMArena Text: medicine and healthcare | 1368 | Not measured | Only Qwen3 32B measured |
| LMArena Text: legal and government | 1324 | Not measured | Only Qwen3 32B measured |
| LMArena Text: business and finance | 1318 | Not measured | Only Qwen3 32B measured |
| Neither model has a result on the other 15 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index17 boards measured
| Benchmark | Qwen3 32B | ReMM SLERP 13B | Leader |
|---|---|---|---|
| IFBench | 36.3% | Not measured | Only Qwen3 32B measured |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only Qwen3 32B measured |
| DTBench | 67.5% | Not measured | Only Qwen3 32B measured |
| LMCA | 17.3% | Not measured | Only Qwen3 32B measured |
| Fiction.LiveBench | 74.2% | Not measured | Only Qwen3 32B measured |
| LMArena Text | 1340 | Not measured | Only Qwen3 32B measured |
| LMArena Text: instruction following | 1305 | Not measured | Only Qwen3 32B measured |
| LMArena Text: creative writing | 1297 | Not measured | Only Qwen3 32B measured |
| LMArena Text: English | 1359 | Not measured | Only Qwen3 32B measured |
| LMArena Text: non-English | 1317 | Not measured | Only Qwen3 32B measured |
| LMArena Text: Chinese | 1357 | Not measured | Only Qwen3 32B measured |
| LMArena Text: German | 1341 | Not measured | Only Qwen3 32B measured |
| LMArena Text: Russian | 1311 | Not measured | Only Qwen3 32B measured |
| LMArena Text: multi-turn | 1331 | Not measured | Only Qwen3 32B measured |
| LMArena Text: longer queries | 1327 | Not measured | Only Qwen3 32B measured |
| LMArena Text: writing and language | 1298 | Not measured | Only Qwen3 32B measured |
| LMArena Text: entertainment and media | 1279 | Not measured | Only Qwen3 32B measured |
| Neither model has a result on the other 31 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
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B 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 | Qwen3 32B | ReMM SLERP 13B |
|---|---|---|
| Maker | Qwen | Undi95 |
| Weights | Open weights | Open weights |
| Released | April 28, 2025 | July 22, 2023 |
| Context window | 131K | 6K |
| Input $/Mtok | $0.14 | $0.35 |
| Output $/Mtok | $0.40 | $0.65 |
| 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 Qwen3 32B better than ReMM SLERP 13B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 32B for output tokens 1.6x cheaper ($0.40 vs $0.65 per million) and a larger context window (131K against 6K tokens).
Which is cheaper, Qwen3 32B or ReMM SLERP 13B?
Qwen3 32B costs $0.14 per million input tokens and $0.40 per million output tokens; ReMM SLERP 13B costs $0.35 and $0.65. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 32B or ReMM SLERP 13B?
Qwen3 32B, with 131K tokens against 6K.
Has any benchmark tested both Qwen3 32B and ReMM SLERP 13B?
No benchmark has tested both yet. Qwen3 32B has results on Aider polyglot, LiveCodeBench (Artificial Analysis run) and SciCode; ReMM SLERP 13B has none yet. Compare them on price and context instead. Data as of October 22, 2026.