Qwen3.5 - GAIA
GAIA score 32.6 from TJ-0405
View sourceQwen
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.
Running this yourself: can likely run on your own machine.
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47.7
Quality Score
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Arena ELO
873M
Parameters
262K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
20 of 22 public signals
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2.6M
Downloads
741
Likes
Feb 2026
Released
5/5 signals
3/4 signals
4/5 signals
4/4 signals
4/4 signals
Parameters
800M
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Source-reported access: Open weights (restricted use) · Confident confidence
Gaps we are still tracking
Benchmarks
6
Open Source
1
Research
1
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
GAIA score 32.6 from TJ-0405
View sourceSWE-Bench Verified resolved rate 69.6
View sourceSWE-Bench Verified resolved rate 69.6
Qwen published benchmark or leaderboard evidence for Qwen3.5-0.8B.
View sourceGAIA score 44.2 from WA0824
View sourceWe introduce WriteSAE, the first sparse autoencoder that decomposes and edits the matrix cache write of state-space and hybrid recurrent language models, where residual SAEs cannot reach. Existing SAEs read residual streams, but Gated DeltaNet, Mamba-2, and RWKV-7 write to a d_k times d_v cache through rank-1 updates k_t v_t^top that no vector atom can replace. WriteSAE factors each decoder atom into the native write shape, exposes a closed form for the per-token logit shift, and trains under matched Frobenius norm so atoms swap one cache slot at a time. Atom substitution beats matched-norm ablation on 92.4% of n=4{,}851 firings at Qwen3.5-0.8B L9 H4, the 87-atom population test holds at 89.8%, the closed form predicts measured effects at R^2=0.98, and Mamba-2-370M substitutes at 88.1% over 2,500 firings. Sustained three-position installs at 3times lift midrank target-in-continuation from 33.3% to 100% under greedy decoding, the first behavioral install at the matrix-recurrent write site.
Qwen3.5-0.8B is now available through local Ollama runtime. 40K context window listed. Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
SWE-Bench Verified resolved rate 69.6
SWE-Bench Verified resolved rate 69.6
Qwen published benchmark or leaderboard evidence for Qwen3.5-0.8B.