Model comparison
DeepSeek V4-Flash vs Ling 3.0 Flash
DeepSeek against InclusionAI, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
DeepSeek
DeepSeek V4-Flash
Budget · Open weights
InclusionAI
Ling 3.0 Flash
Budget · Open weights
| Specification | DeepSeek V4-Flash | Ling 3.0 Flash |
|---|---|---|
| Provider | DeepSeek | InclusionAI |
| Tier | Budget | Budget |
| Context window | Winner: 1M | 262K |
| Max output | 384K | Not verified |
| Input / 1M tokens | $0.14 | Winner: $0.075 |
| Output / 1M tokens | $0.28 | Winner: $0.22 |
| Weights | Open | Open |
| Parameters | 284B total / 13B active (MoE) | 124B total / 5.1B active (MoE) |
| Reasoning levels | low, high, max | Not verified |
| Modalities | text | text |
| Released | July 31, 2026 | July 24, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | Winner: 52 | 38 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: DeepSeek V4-Flash: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC.
DeepSeek V4-Flash
Open-weight price-performance pick: MIT weights, 1M context, and API rates far below closed budget tiers.
Best for
- Cost-sensitive hosted agents
- High-volume coding assist
- Open-weight deployments with cluster VRAM
Watch out
API list prices move to peak/off-peak from 2026-08-16 16:00 UTC (see pricing note). Self-hosting the ~284B MoE still needs roughly 90–170GB+ class VRAM depending on quant — not a laptop budget build. DeepSWE snapshot also uses the shared mini-swe-agent harness — verify cost, effort, and serving conditions before treating the result as a forecast.
Ling 3.0 Flash
InclusionAI's cost-focused Flash tier for high-frequency hybrid reasoning and agent loops at low list rates.
Best for
- High-volume agent loops
- Cost-sensitive chat and extraction
- Fast hybrid reasoning
Watch out
Native context is 256Ki tokens (262,144). Confirm serving provider and current list before committing volume.
Benchmark
DeepSWE 1.1 in context
Only DeepSeek V4-Flash has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
DeepSeek V4-Flash vs Ling 3.0 Flash
Answered from the verified figures on this page rather than general guidance.
Is DeepSeek V4-Flash or Ling 3.0 Flash cheaper for input?
Ling 3.0 Flash is cheaper at $0.075 per million input tokens, against $0.14 for DeepSeek V4-Flash — roughly 1.9× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4-Flash has tiered pricing: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC.
Is DeepSeek V4-Flash or Ling 3.0 Flash cheaper for output?
Ling 3.0 Flash is cheaper at $0.22 per million output tokens, against $0.28 for DeepSeek V4-Flash — roughly 1.3× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4-Flash has tiered pricing: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC.
Which has the larger context window, DeepSeek V4-Flash or Ling 3.0 Flash?
DeepSeek V4-Flash accepts 1M tokens against 262K for Ling 3.0 Flash. This only matters if you routinely send very long documents or large codebases.
Should I use DeepSeek V4-Flash or Ling 3.0 Flash?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. DeepSeek V4-Flash suits cost-sensitive hosted agents; Ling 3.0 Flash suits high-volume agent loops.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.