Model comparison
DeepSeek V4-Flash vs Kimi K2.7 Code
DeepSeek against Moonshot AI, compared on context, price, and verified benchmark results.
Catalog record checked August 19, 2026; individual provider fields may change.
DeepSeek
DeepSeek V4-Flash
Budget · Open weights
Moonshot AI
Kimi K2.7 Code
Budget
| Specification | DeepSeek V4-Flash | Kimi K2.7 Code |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Tier | Budget | Budget |
| Context window | Winner: 1M | 262K |
| Max output | 384K | Not verified |
| Input / 1M tokens | Not verified | $0.95 |
| Output / 1M tokens | Not verified | $4 |
| Weights | Open | Closed |
| Parameters | 284B total / 13B active (MoE) | Not disclosed |
| Reasoning levels | low, high, max | Not verified |
| Modalities | text | text |
| Released | July 31, 2026 | July 22, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-14) | Winner: 52 | 43 |
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: No single standard-tier rate after 2026-08-16 16:00 UTC. Official peak/off-peak (cache miss / output): off-peak $0.22/$0.66, peak $0.44/$1.32. Peak hours are 01:00–04:00 and 06:00–10:00 UTC. Expired intro $0.14/$0.28 is not shown as the list price. · Kimi K2.7 Code: Moonshot coding SKU `kimi-k2.7-code`: cache-hit input $0.19 / MTok, cache-miss $0.95 / MTok, output $4.00 / MTok. Highspeed sibling `kimi-k2.7-code-highspeed` is $1.90 / $8.00 (cache-hit $0.38) — not this row.
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
Hosted billing is peak/off-peak (see pricing note), not a flat intro rate. 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.
Kimi K2.7 Code
Moonshot's dedicated coding API identity — same $0.95/$4.00 miss/output band as Kimi K2.6, but a distinct product buyers pick for coding agents.
Best for
- Hosted coding agents
- Moonshot coding workloads
- Cost-sensitive code completion
Watch out
Not a drop-in for Kimi K3 or K2.6 general chat. Highspeed is a separate, higher-priced id. Do not invent open-weight status — this row is the hosted coding API.
Benchmark
DeepSWE 1.1 in context
Both models 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
One of these rows is missing a verified output price, so this page will not name a cheaper winner. Run the model picker against the actual job instead of guessing.
Run the model pickerCommon questions
DeepSeek V4-Flash vs Kimi K2.7 Code
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, DeepSeek V4-Flash or Kimi K2.7 Code?
DeepSeek V4-Flash accepts 1M tokens against 262K for Kimi K2.7 Code. This only matters if you routinely send very long documents or large codebases.
Should I use DeepSeek V4-Flash or Kimi K2.7 Code?
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; Kimi K2.7 Code suits hosted coding agents.
Can I self-host DeepSeek V4-Flash or Kimi K2.7 Code?
DeepSeek V4-Flash publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Kimi K2.7 Code 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.