Model comparison
Gemini 3.5 Flash vs Ling 3.0 Flash
Google against InclusionAI, compared on context, price, and verified benchmark results.
Catalog record checked August 18, 2026; individual provider fields may change.
Gemini 3.5 Flash
Budget
InclusionAI
Ling 3.0 Flash
Budget · Open weights
| Specification | Gemini 3.5 Flash | Ling 3.0 Flash |
|---|---|---|
| Provider | InclusionAI | |
| Tier | Budget | Budget |
| Context window | Winner: 1.05M | 262K |
| Max output | 66K | Not verified |
| Input / 1M tokens | $1.50 | Winner: $0.075 |
| Output / 1M tokens | $9 | Winner: $0.22 |
| Weights | Closed | Open |
| Parameters | Not disclosed | 124B total / 5.1B active (MoE) |
| Reasoning levels | Not verified | Not verified |
| Modalities | text, image, video, audio, pdf | text |
| Released | May 19, 2026 | July 24, 2026 |
| Artificial Analysis Intelligence Index (2026-08-14) | Not verified | 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: Gemini 3.5 Flash: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card.
Gemini 3.5 Flash
Google’s generally available Gemini 3.5 Flash tier: 1M context, thinking-enabled, and the stable alias behind `gemini-flash-latest`. Distinct from Flash-Lite (cheaper throughput) and from 3.6/3.7 Flash (newer workhorses).
Best for
- Stable Google Flash alias
- Agent loops that must pin a GA id
- Multimodal volume when 3.7 is not required
Watch out
Do not treat this as Flash-Lite or as 3.7 Flash. Standard paid list is $1.50/$9; DeepSWE 1.1 high-effort is 36.1% at $3.45/task. Model card latest update: May 2026.
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 Gemini 3.5 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.
When the cheaper one wins
Ling 3.0 Flash is cheaper on output at $0.22 per million tokens against $9 for Gemini 3.5 Flash — about 41×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerCommon questions
Gemini 3.5 Flash vs Ling 3.0 Flash
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.5 Flash or Ling 3.0 Flash cheaper for input?
Ling 3.0 Flash is cheaper at $0.075 per million input tokens, against $1.50 for Gemini 3.5 Flash — roughly 20× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.5 Flash has tiered pricing: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card.
Is Gemini 3.5 Flash or Ling 3.0 Flash cheaper for output?
Ling 3.0 Flash is cheaper at $0.22 per million output tokens, against $9 for Gemini 3.5 Flash — roughly 41× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.5 Flash has tiered pricing: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card.
Which has the larger context window, Gemini 3.5 Flash or Ling 3.0 Flash?
Gemini 3.5 Flash accepts 1.05M tokens against 262K for Ling 3.0 Flash. This only matters if you routinely send very long documents or large codebases.
Should I use Gemini 3.5 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. Gemini 3.5 Flash suits stable google flash alias; Ling 3.0 Flash suits high-volume agent loops.
Can I self-host Gemini 3.5 Flash or Ling 3.0 Flash?
Ling 3.0 Flash publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.5 Flash is a closed model whose supported access paths are controlled by its provider.
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.