DeepSeek V4.1 Flash vs Claude Opus 5 vs GPT-5.6 Sol: Complete Comparison and Report on Pricing, Benchmarks, Context Window, and Open-Weight Access

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DeepSeek released V4.1 Flash on September 10, 2026, and named its two comparison targets directly in the launch material: Claude Opus 5 and GPT-5.6 Sol. That is unusual. Most model launches compare against an unnamed "leading frontier model" or against the vendor's own prior generation. DeepSeek instead published a specific claim, on a specific benchmark, against two named competitors, at a price that differs from theirs by roughly three orders of magnitude on cached input.

This report treats that claim as the starting point, verifies what it does and does not cover, and sets it against the two models' own published specifications.

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RELEASE TIMELINE AND MODEL IDENTITY.

Model identifiers, lineage, and licensing terms.

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Attribute	DeepSeek V4.1 Flash	Claude Opus 5	GPT-5.6 Sol
Vendor	DeepSeek	Anthropic	OpenAI
Release date	September 10, 2026	July 24, 2026	July 9, 2026
Model ID	deepseek-flash	claude-opus-5	gpt-5.6-sol
Predecessor	DeepSeek-V4-Flash (284B parameters, 13B active)	Claude Opus 4.8	GPT-5.5
Parameter count	552B total, 8B active on input, 16B active on output	Not disclosed	Not disclosed
Architecture	Causal Encoder-Decoder, mixture-of-experts	Not disclosed	Not disclosed
License	MIT, open weights on Hugging Face	Proprietary	Proprietary
Naming signal	"Flash" implies a V4.1 Pro is still to come	Sole flagship at this tier	Flagship of the GPT-5.6 family (Sol/Terra/Luna)

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DeepSeek's own framing supports the naming signal in the table: the company describes V4.1 Flash as the smallest model in its new architecture family, which is a direct statement that a larger sibling is planned. Neither Opus 5 nor Sol carries an equivalent internal signal of a near-term successor at the time of this report.

The parameter disclosure gap is total in one direction. DeepSeek publishes exact total and active parameter counts as a matter of course, consistent with open-weight practice. Anthropic and OpenAI disclose neither figure for their current flagships, which means any efficiency comparison in this report can state DeepSeek's compute cost precisely and can only estimate the other two.

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PRICING AND THE PEAK/OFF-PEAK STRUCTURE.

A pricing mechanism that has no equivalent at either closed-model vendor.

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Rate per 1M tokens	DeepSeek V4.1 Flash (off-peak)	DeepSeek V4.1 Flash (peak)	Claude Opus 5	GPT-5.6 Sol
Cached input	$0.003	$0.006	$0.50	$0.50
Uncached input	$0.15	$0.30	$5	$5 (discounted 20%+ since Aug 21)
Output	$0.60	$1.20	$25	$30 (discounted 20%+ since Aug 21)

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At off-peak rates, DeepSeek V4.1 Flash's cached input costs roughly 1/167th of Opus 5's and Sol's, and its uncached input costs roughly 1/33rd. Even at peak pricing, double the off-peak rate, the gap to either closed model remains above tenfold on every line.

Neither Opus 5 nor Sol has a time-of-day pricing tier. DeepSeek's off-peak discount follows Beijing business hours, a scheduling detail with a direct operational consequence for teams outside China: a batch job scheduled for a US or European overnight window may run during DeepSeek's peak pricing period rather than its discount period, and the discount should be checked against Beijing time rather than the requesting team's own time zone before it is built into a cost model.

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CONTEXT WINDOW AND OUTPUT LIMITS.

Window sizes across all three.

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Limit	DeepSeek V4.1 Flash	Claude Opus 5	GPT-5.6 Sol
Context window	1,000,000 tokens	1,000,000 tokens	1,050,000 tokens
Maximum output	Not published in this comparison	128,000 tokens	128,000 tokens
Reasoning tiers	Low, high, max	Low, medium, high	Low, medium, high, xhigh, max
Long-context billing threshold	None published	None	Above 272,000 input tokens: 2x input, 1.5x output

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All three sit within 5% of each other on context window size, which removes window size as a differentiator in this comparison. Sol's long-context penalty above 272,000 tokens has no equivalent at DeepSeek or Anthropic, and at DeepSeek's price level the penalty would be close to immaterial in absolute dollar terms even if one existed.

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BENCHMARK METHODOLOGY AND SOURCE RELIABILITY.

What DeepSeek's own comparison covers, and what independent verification exists so far.

DeepSeek's launch claim is specific enough to check: it names Terminal-Bench 2.1 and DeepSWE v1.1 as the benchmarks where V4.1 Flash beats both named competitors, and it separately discloses that the same model trails both on Terminal-Bench 4.0 and GPQA Diamond. A vendor naming its own losses in the same announcement as its wins is a stronger disclosure practice than a launch table that only shows favorable comparisons, and it is worth crediting as such.

The benchmark figures themselves remain vendor-reported. No independent evaluator's re-run of Terminal-Bench 2.1 across all three models was located for this report, and coverage published in the days following the launch explicitly flagged this: every number circulating for DeepSeek V4.1 Flash, alongside two other models released the same week, was vendor-reported rather than independently confirmed at time of writing.

A separate data point affects how much confidence to place in DeepSeek's own numbers holding steady after launch. DeepSeek initially announced that all deepseek-v4-pro API requests would be rerouted to serve from V4.1 Flash starting September 14, at V4.1 Flash pricing, only to reverse that decision on the eve of the change and keep V4 Pro serving separately with unchanged billing. A benchmark claim from a vendor that reversed a routing and pricing decision within four days of announcing it is not necessarily wrong, but it does indicate a company still actively adjusting its own product boundaries in real time.

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DIRECTLY COMPARABLE SCORES: WHERE DEEPSEEK'S OWN CLAIM HOLDS.

The two benchmarks DeepSeek named as wins.

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Benchmark	DeepSeek V4.1 Flash	Claude Opus 5	GPT-5.6 Sol
Terminal-Bench 2.1	90.6%	89.1%	88.8%
DeepSWE v1.1	Ahead of both, per DeepSeek (exact score not published in this comparison)	Not published in this comparison	Not published in this comparison

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The Terminal-Bench 2.1 gap between all three is under two points, with DeepSeek's claimed lead over Sol at 1.8 points and over Opus 5 at 1.5 points. A gap of that size, reported by the winning party without an independent third-party re-run, should be read as a plausible near-parity result rather than a decisive win, even though it is directionally consistent with DeepSeek's stated claim.

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WHERE DEEPSEEK TRAILS: THE BENCHMARKS THE VENDOR NAMED AS LOSSES.

DeepSeek's own disclosed weaknesses against the same two models.

DeepSeek's launch material states directly that V4.1 Flash trails both Opus 5 and Sol on Terminal-Bench 4.0 and on GPQA Diamond, without publishing the specific scores for any of the three models on either benchmark in the material reviewed for this report. Terminal-Bench 4.0 is a materially harder revision than 2.1, the version where DeepSeek claims parity, so the two results together describe a model that performs close to frontier level on an established benchmark version while trailing on the newer, harder one and on graduate-level science questions.

That pattern, competitive on established mid-difficulty benchmarks, trailing on the hardest and most recent ones, is a normal profile for a smaller active-parameter model rather than an unusual one, given DeepSeek's own disclosure that only 8 billion of its 552 billion total parameters activate per input token.

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CYBERSECURITY BENCHMARK STANDING.

A result that connects to this outlet's separate coverage of gated cyber-capable models.

On the CyberGym benchmark, which measures reproduction of real historical vulnerabilities across 188 software projects, DeepSeek V4.1 Flash leads the published snapshot at 88.1%, ahead of Sakana AI's Fugu Cyber at 86.9% and the Shanghai AI Laboratory's Atria Dawn Preview at 86.5%. Neither Opus 5 nor Sol appears in that specific published ranking in the material reviewed for this report; this outlet's separate coverage of gated cyber models placed Claude Mythos 5.1, running on Opus-generation weights without cyber safeguards, at 83.1% on the same benchmark using an Anthropic-reported figure from a different testing round.

Reading these two figures together needs a caveat: an 88.1% score with no safeguards removed, from an open-weight model with no gating program at all, sitting close to or above a safeguard-removed flagship's score, says as much about how narrow the top of this specific leaderboard currently is as it does about either model's absolute capability.

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INFRASTRUCTURE EFFICIENCY: THE KV CACHE ENGINEERING.

A specific technical claim with no equivalent disclosure at the other two vendors.

DeepSeek discloses a global key-value cache of 890 bytes per token, which the company states is a quarter the size of its own prior V4-Flash model's cache and 437 times smaller than its original V1 model's. This is achieved through a split encoder-decoder architecture, layer-sharing sparse attention, and FP4 quantization, all disclosed in DeepSeek's own technical materials.

A smaller KV cache directly reduces the memory cost of serving long-context, multi-turn agentic sessions, which is the specific workload category where cache size, not raw parameter count, tends to set the practical ceiling on how many concurrent sessions a given amount of hardware can serve. Neither Anthropic nor OpenAI publishes an equivalent cache-size figure for Opus 5 or Sol, so this specific efficiency claim cannot currently be checked against either competitor, only stated as DeepSeek's own disclosed number.

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SELF-HOSTING VERSUS MANAGED API ACCESS.

A structural option only one of these three models offers.

DeepSeek V4.1 Flash ships under an MIT license with weights published on Hugging Face and support for vLLM, SGLang, and Transformers, alongside DeepSeek's own hosted API with low, high, and max reasoning tiers. Opus 5 and Sol are available only as managed API and consumer-app access; neither vendor publishes weights or supports self-hosted deployment at any tier.

This gives DeepSeek V4.1 Flash a deployment option the other two cannot match regardless of price: an organization with data-residency or air-gapped infrastructure requirements can run the model entirely inside its own environment, at the cost of provisioning and maintaining the serving infrastructure itself, a cost that does not appear in DeepSeek's published per-token pricing at all.

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AVAILABILITY AND PLATFORM SUPPORT.

Where each model can be reached today.

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Channel	DeepSeek V4.1 Flash	Claude Opus 5	GPT-5.6 Sol
Self-hosted	Yes, MIT license, Hugging Face weights	Not available	Not available
Vendor API	DeepSeek API	Anthropic API	OpenAI API
Third-party partner integration	WorkBuddy (including CodeBuddy), OpenCode	Claude Code, Claude Cowork	Codex
Cloud platforms	Not specified in this comparison	Amazon Bedrock, Google Vertex AI, Microsoft Foundry	Amazon Bedrock, Azure
Consumer app	Not specified as a ChatGPT/Claude-style consumer default	Claude.ai, default on Claude Max	ChatGPT Plus, Pro, Business, Enterprise

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Opus 5 has the broadest documented enterprise cloud footprint of the three, present on all three major clouds. DeepSeek's advantage is architectural rather than platform-based: the self-hosting option functions as its own distribution channel, independent of any cloud marketplace listing.

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PRICE VOLATILITY AND ROADMAP SIGNALS.

What each vendor's recent behavior suggests about pricing stability going forward.

All three models sit inside an active pricing adjustment window at the time of this report. DeepSeek cut V4.1 Flash's own prices effective September 10 alongside the model's release, then reversed a separate plan to migrate V4 Pro traffic onto the new pricing four days later. Sol's price has been running at a stated 20%-plus discount since August 21, confirmed only through at least November 21, after which the rate this report uses may no longer apply. Opus 5 is the most stable of the three on this dimension: its pricing is unchanged from its predecessor Opus 4.8, with no discount window or scheduled change identified in the material reviewed for this report.

Given DeepSeek's own "Flash" naming and its disclosure that this is the smallest model in a new architecture family, a V4.1 Pro release should be treated as a near-term certainty rather than a possibility, which means any procurement decision anchored to V4.1 Flash's current benchmark standing should be revisited once that larger sibling ships.

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WORKLOAD ALLOCATION CRITERIA.

What DeepSeek's own disclosed strengths and weaknesses actually support as a decision.

DeepSeek V4.1 Flash's verified case rests on Terminal-Bench 2.1 and DeepSWE v1.1, where its own disclosure shows near-parity with two named flagships at a fraction of the price, plus a genuine architectural option, MIT-licensed self-hosting, that neither competitor offers at any price. That combination makes it the reasonable first model to benchmark against for high-volume agentic coding work on established task types, particularly where self-hosting or data residency is a hard requirement rather than a preference, provided the Beijing-hours pricing schedule is checked against the actual workload's run times.

DeepSeek's own disclosure that it trails on Terminal-Bench 4.0 and GPQA Diamond means it should not be assumed to hold up on newer, harder task formulations or on graduate-level science reasoning without direct testing, and no team should treat a benchmark won by four points or fewer as decisive until an independent source reproduces it.

Opus 5 and Sol remain the better-evidenced choice for workloads where the harder benchmark versions matter, where enterprise cloud integration across multiple providers is required, or where a stable, undiscounted price is preferable to a cheaper rate that depends on a discount window with a stated end date. Given that DeepSeek is the only one of the three to name its own losses in its own launch material, and that all three vendors currently have live pricing or naming signals suggesting near-term change, the specific rates and scores in this report should be re-verified against each vendor's current documentation before any multi-month cost projection is built on them.

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DATA STUDIOS

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[datastudios.org]